Abstract:
A computer-implemented method for automating manufacturing supply chain planning, comprising: (a) providing a computer processor for processing data; (b) providing at least one input device; (c) providing at least one output device; (d) providing a computer readable storage device; (e) providing a first ontology for defining a product in terms of the method of manufacture of said product or for defining a plurality of products in terms of the method of manufacture of said products; (f) providing a second ontology for defining the capabilities of a plurality of manufacturing facilities; and g) providing a knowledge representation and reasoning system executed on said computer processor.

Description:
COPYRIGHT NOTICE 
       [0001]    A portion of the disclosure of this patent document contains material, which is subject to (copyright or mask work) protection. The (copyright or mask work) owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all (copyright or mask work) rights whatsoever. 
       CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0002]    The present application claims priority to Australian Provisional Patent Application serial number 2016901133, filed on Mar. 28, 2016 and Australian Provisional Patent Application serial number 2016901519, filed on 25 Apr. 2016 and U.S. Provisional Patent Application Ser. No. 62/345,887, filed on Jun. 15, 2016 and U.S. Provisional Patent Application Ser. No. 62/344,415, filed on Jun. 2, 2016, the disclosures of which are hereby incorporated in their entirety at least by reference. 
       TECHNICAL FIELD 
       [0003]    The present disclosure relates generally to systems and methods for optimizing manufacturing supply-chains using a knowledge representation and reasoning system. 
       BACKGROUND 
       [0004]    Models of manufacturing have become increasingly complex along supply chains spanning the globe. With the advent of robotics and advanced manufacturing, individual aspects of this supply-chain have become increasingly automated. Awaiting full automation is the supply-chain itself. Automated reasoning about product design and connecting this to robotic capabilities is a step in that direction and the subject of this disclosure. 
         [0005]    The Knowledge Interchange Format (KIF) is a computer language designed to enable systems to share and re-use information from knowledge systems. 
         [0006]    An ontology is a system for the formal definition of types, and/or properties, and/or interrelationships of entities that exist for a particular domain of discourse. The study of Artificial Intelligence (AI) creates ontologies to limit complexity and to organize information. The ontology can then be applied to automated problem solving. 
         [0007]    A knowledge representation and reasoning system is a language and environment for constructing intelligent, knowledge-based applications. A typical knowledge representation and reasoning system incorporates features from predicate calculus, a branch of discrete mathematics. Advanced systems complement this discrete mathematics with functional programming concepts, relational database concepts, and quantitative models from the domain of statistics. The PowerLoom® Knowledge Representation &amp; Reasoning System is a knowledge representation and reasoning system by the University of Southern California, that employs the Knowledge Interchange Format. 
         [0008]    Build-Your-Own-Blocks is a framework to create Domain Specific Languages as an extension to Scratch. Scratch is a visual programming language where one can create interactive stories, games, and animations. Build-Your-Own-Blocks and Scratch allow basic syntax constructs of a programming language to be represented graphically in a paint-on-a-canvas style application. The approach is primarily targeted at children in educational settings, but it has yet to find wide application in an industrial setting, largely owing to limited expressiveness of the tools and language. 
       BRIEF SUMMARY OF THE INVENTION 
       [0009]    The described technology concerns one or more methods and systems to enable artificial intelligence supported product design in an automated manufacturing setting employing the use of robots. The use cases supported by the described technology are as described. Given a population of robots and a systematic product description, the described technology will be able to do the following:
       a) Answer the question as to whether a product can be built: a feasibility analysis   b) Detail the exact operations required to build a product end-to-end   c) Formulate a manufacturing plan describing the robots required to build a product   d) Apply optimization constraints to feasibility analyses and manufacturing plans       
 
         [0014]    In various embodiments, the described technology combines a robotic capability model with a manufacturing ontology in an artificial intelligence reasoning system that supports classification of robotic capabilities and formulation of product descriptions in terms of the manufacturing steps utilizing these robotic capabilities and which are needed to produce the various products. 
         [0015]    The output of the system is a sequence of steps required to produce the product in question, which satisfy and optimize given specifications. The method is designed to formulate manufacturing plans for bespoke products. This enables intelligent product design by consumers, application software, and manufacturers alike with a view towards supply chain automation, in particular with a view towards a pull supply-chain model. 
         [0016]    In support of the above use-cases, we present a robotic capability ontology and a manufacturing ontology unified in a knowledge representation and reasoning system. The knowledge representation and reasoning system allows the application of constraints and statistical prediction models through a rule based deduction engine with a view towards optimizing the automated manufacture of products by robotic facilities. We further integrate deductive reasoning with qualitative and quantitative reasoning to allow optimization of manufacturing plans in real world settings. Additionally we present a method of making the described technology accessible to non-technical users through visual modeling. 
         [0017]    Many of the details, functions and other features shown and described in conjunction with this description are illustrative implementations of particular embodiments of the present disclosure. Accordingly, other embodiments can have other details, functions and features without departing from the spirit and scope of the present disclosure. In addition, those of ordinary skill in the art will appreciate that further embodiments of the present disclosure can be practiced without several of the details described below. 
         [0018]    Certain details are set forth in the descriptions of  FIGS. 1-14  to provide a thorough understanding of various embodiments of the present disclosure. A person of ordinary skill in the relevant art will understand that the present disclosure may have additional embodiments that may be practiced without several of the details described below. In other instances, those of ordinary skill in the relevant art will appreciate that the methods and systems described can include additional details without departing from the spirit or scope of the disclosed embodiments. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0019]      FIG. 1  is a diagram showing the components of the described technology in one embodiment of the present invention. 
           [0020]      FIG. 2  is a use case diagram showing how users might interact with the described technology in one embodiment of the present invention. 
           [0021]      FIG. 3  is a flow diagram describing the definition of ontology as a structured knowledge graph in the described technology in one embodiment of the present invention. 
           [0022]      FIG. 4  is a flow diagram showing how machine learning is applied to a structured knowledge graph in the described technology in one embodiment of the present invention. 
           [0023]      FIG. 5  is a hardware diagram showing components of a typical computer system on which the described technology executes in one embodiment of the present invention. 
           [0024]      FIG. 6  is a diagram depicting an example environment within which the described technology may execute in one embodiment of the present invention. 
           [0025]      FIG. 7  shows an ontology hierarchy browser in one embodiment of the present invention. 
           [0026]      FIG. 8  shows a product graph in an ontology browser in one embodiment of the present invention. 
           [0027]      FIG. 9  shows visual product modeling using a domain specific language in one embodiment of the present invention. 
           [0028]      FIG. 10  is a block diagram describing geometric syntax verification in visual programming in one embodiment of the present invention. 
           [0029]      FIG. 11  is a block diagram describing higher order visual programming with detail hiding in one embodiment of the present invention. 
           [0030]      FIG. 12  is a flow diagram describing artificial intelligence assisted visual live programming in one embodiment of the present invention. 
           [0031]      FIG. 13  is a block diagram describing database entity to logic object type coercion mechanism in one embodiment of the present invention. 
           [0032]      FIG. 14  is a block diagram describing MIL class to logic object type coercion mechanism in one embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0033]    Referring to the system overview  100  of  FIG. 1 , the described technology employs a Knowledge Representation and Reasoning System  120  and optionally a Machine Learning Subsystem  190 , coupled with a robotic capability ontology  150  and a manufacturing ontology  160 . The robotic capability ontology  150  may optionally be formulated in Knowledge Interchange Format (“KIF”) robot definitions  110 . The manufacturing ontology  160  may optionally be formulated as Knowledge Interchange Format (“KIF”) product specifications  130 . Input  165  to the Knowledge Representation and Reasoning System  120  are product queries  180 . Product Queries  180  are read by the Knowledge Representation and Reasoning System  120  which computes  145  through the Manufacturing Ontology  160  and further computes  140  through the Robotic Capability Ontology  150  the resulting outputs  175  and furnishes manufacturing plans  185  that satisfy the product specifications in the product queries  180 . An optional Knowledge Visualization Subsystem  125  displays the robotic capability ontology  150  and the manufacturing ontology  160 . Further, an optional Visual Programming Environment  135  allows visual manipulation of product designs by non-technical users. 
         [0034]      FIG. 2  is a use case diagram  200  showing how users might interact with the described technology. Vendors  210  provide robotic capabilities (robotic definitions)  215  which are received by the Knowledge Representation and Reasoning System  225 . Product designers  230  provide product and sub-product specifications  235  which are received by the Knowledge Representation and Reasoning System  225 . A user  240  submits product queries  245  detailing constraints and optimizations  265  to the Knowledge Representation and Reasoning System  225 . The user  240  may or may not be the same as the product designer  230 . The Knowledge Representation and Reasoning System  225  then computes the manufacturing plan  250  and provides this to the user  260 . User  240  may or may not be the same user as user  260 . User  260  may then optionally initiate production  255  of the product according to the manufacturing plan with one or more vendors  220 . Users  240  and  260  may or may not be human agents and may be robotic algorithms executing as computer software. 
         [0035]      FIG. 3  is a flow diagram  300  describing the definition of ontology as a structured knowledge graph in the described technology. The described technology comprises two ontologies: the robotic capability ontology and the manufacturing ontology. Each ontology is defined as follows. Step  305  decides if a new concept is to be created. If a new concept is to be created, step  310  decides if the new concept is a root concept, i.e. one without a parent concept, also referred to as a base concept. If the new concept is a root concept, the new concept is created as such  315 . Table 3 shows an example of a root or base concept being defined. If the new concept is not a root concept, the new concept is defined in terms of its antecedent or parent concept  320 . Table 4 shows examples of concepts defined in terms of their parent concepts. Those of ordinary skill in the in the art will recognize concepts defined in terms of their antecedents or parent concepts as semantics akin to object oriented inheritance and therefore akin to specialization and generalization, but different in that concepts are first class citizens of predicate calculus. This enables the system to reason about predicates and propositions in terms of specialization and generalization, which is a critical feature of building a knowledge graph, that can be computed. If a new relation is to be created  325 , step  330  creates a new relation. Those of ordinary skill in the art will recognize relations as akin to relational database semantics, but different in that here relations are first class citizens of predicate calculus which become part of a knowledge graph, which can be computed. Relations assist in logical categorization and classification. Table 5 shows examples of relations being defined. If a new function is to be created  335 , step  340  creates a new function. Those of ordinary skill in the art will recognize functions as akin to functional programming semantics, but different since here they are first class citizens of predicate calculus which become part of a knowledge graph, which can be computed. Table 15 shows an example of a function being defined. New qualifications, quantifications and categorizations may be defined  345  by defining propositions or predicates elaborating zero or more concepts, zero or more relations, and zero or more functions  350 . Step  350  connects concepts, relations and functions in terms of predicate calculus through constructs such as universal and existential quantification. Table 16 demonstrates elaborating a relation through universal and existential quantification. 
         [0036]      FIG. 4  is a flow diagram  400  describing how machine learning is applied to a structured knowledge graph in the described technology  410 . If a regression model is to be created  420 , step  430  defines a neural network across the structured knowledge graph. Table 34 demonstrates defining a neural network across a structured knowledge graph. Step  450  trains the neural network on sample data relating to the structured knowledge graph. Predictive queries are now available  470 . Predictive queries can be used to approximate or predict properties for which otherwise no data has been recorded. Table 38 demonstrates an example of a predictive query. Crucially, predictive queries can also be used to quantify what impact changes in some properties will have on other properties. If no regression model is to be created  420 , step  440  defines a probabilistic neural network. Step  460  trains the neural network on sample data relating to the structured knowledge graph. Probabilistic queries are now available  480 . Probabilistic queries are useful in categorization and classification problems where relevant data is missing. Likewise, probabilistic queries can be used to determine how likely changes in one property will lead to changes in another. Predictive queries and probabilistic queries combine to be a powerful tool in optimizing for specific outcomes. 
         [0037]      FIG. 5  and the following discussion provide a brief general description of a suitable computing environment in which aspects of the described technology can be implemented. Although not required, aspects of the technology may be described herein in the general context of computer-executable instructions, such as routines executed by a general—or special purpose data processing device (e.g. a server or client computer). Aspects of the technology described herein may be stored or distributed on tangible computer-readable media, including magnetically or optically readable computer discs, hard-wired or preprogrammed chips (e.g., EEPROM semiconductor chips), nanotechnology memory, biological memory, or other data storage media. Alternatively, computer implemented instructions; data structures, screen displays, and other data related to the technology may be distributed over the Internet or over other networks (including wireless networks) on a propagated signal on a propagation medium (e.g. an electromagnetic wave, a sound wave etc.) over a period of time. In some implementations, the data may be provided on any analog or digital network (e.g., packet-switched, circuit-switched, or other scheme). 
         [0038]    The described technology can also be practiced in distributed computing environments, where tasks or modules are performed by remote processing devices, which are linked through a communications network, such as a Local Area Network (“LAN”), Wide Area Network (“WAN”), or the Internet. In a distributed computing environment, program modules or subroutines may be located in both local and remote memory storage devices. Those skilled in the relevant art will recognize that portions of the described technology may reside on a server computer, while corresponding portions reside on a client computer (e.g., PC, mobile computer, tablet, or smart phone). Data structures and transmissions of data particular to aspects of the technology are also encompassed within the scope of the described technology. 
         [0039]    Referring to  FIG. 5 , the described technology employs a computer  500 , such as a personal computer, workstation, phone, or tablet, having one or more processors  520  coupled to one or more user input devices  540  and data storage devices  550 . The computer  500  is also coupled to at least one output device  560 , such as a display  570 . The computer  500  may be coupled to external computers, such as via an optional network connection  530 , a wireless transceiver  510 , or both. For example, network hubs, switches, routers, or other hardware network components within the network connection  530  and/or wireless transceiver  510  can couple one or more computers  500 . 
         [0040]    The input devices  540  may include a keyboard and/or a pointing device such as a mouse. Other input devices are possible. The storage devices  550  may include any type of computer-readable media that can store data accessible to the computer  500 , such as magnetic hard and floppy disk drives, optical disc drives, magnetic cassettes, tape drives, flash memory cards, digital video disks (DVDs), Bernoulli cartridges, RAMs, ROMs, smart cards, etc. Indeed, any medium for storing or transmitting computer-readable instructions and data may be employed, including a connection port to a node on a network, such as LAN, WAN, or the Internet (not shown in  FIG. 5 ). 
         [0041]      FIG. 6  is a diagram illustrating an example environment  600  within which the described technology may operate. Environment  600  may include operator terminals (nodes)  610  and  640 , client computers (nodes)  660  on a network  630  from which operators may enter robotic capabilities, product specifications or request and receive manufacturing plans for product specifications. Servers  650 , in some embodiments, are dedicated or partially dedicated nodes that facilitate various aspects of the described technology. Servers  650  may also be coupled to one or more databases  620 . 
         [0042]      FIG. 7  is a diagram illustrating an example ontology browser  700  within which the described technology may operate. The left box  710  displays a tree view of concepts with the root robot concept  730 . Nodes  740 ,  750  and  760  show subordinate categorizations of robots that reflect their hierarchical structure. Relations include both mathematical relations  780  (prefixed with “R”) and mathematical functions  770  (prefixed with “F”). 
         [0043]      FIG. 8  is a diagram is a diagram illustrating an example ontology browser  800  relating concepts  810  to relations  820  (both mathematical relations and mathematical functions), instances of concepts  830  (here instances of Part) and propositions (properties) of instances selected in the browser  840 . Here the “Part” instance “Box” was selected and it was shown that “Case” and “Lid” are parts of “Box.” 
         [0044]      FIG. 9  is a diagram illustrating an example visual programming model  900  of the Box from [0025]  FIG. 8 . The script  910  identifies the program. The pane  970  shows a visual rendering, or simulation, of the finished product. The pane  980  shows constituent parts. Steps  920  and  960  mark the beginning and end of the program. Steps  930 ,  940  and  950  reflect Knowledge Interchange Format descriptions as shown in tables 18, 19, 20 and 21. In steps  930 ,  940  and  950 , the user is assisted through visual cues and only symbols or blocks can be connected that result in syntactically correct constructs. To this end, the external contours of symbols or blocks representing syntactic elements are shaped like puzzle pieces, allowing only matching pieces to be joined. 
         [0045]    Knowledge Representation &amp; Reasoning 
         [0046]    The system described is termed youdobot.net, which implements a Knowledge Representation &amp; Reasoning System. A person of ordinary skill in the relevant art will understand that the present disclosure may be implemented using other knowledge representation &amp; reasoning systems without departing from the spirit or scope of the present disclosure. 
         [0047]    The Knowledge Interchange Format (KIF) in a dialect of the programming language Lisp is used to specify information in a way that a knowledge representation and reasoning system may consume it. Source code shown in tables of the present disclosure employs this dialect of the Lisp programming language. Unlike a relational database, a knowledge representation and reasoning system is fundamentally optimized to define and process rules of inference. Being “optimized to define and process” is also referred to as being a “first class citizen” of an information system. In this vein, inference is a “first class citizen” of a knowledge representation and reasoning system. This leads to an important distinction from traditional database technology. With a relational database, a human must understand and reason about the database schema by formulating queries that reason about the data in the schema. If constraints are to be defined, these are embedded in queries in the form of predicates. In this knowledge representation and reasoning system, by contrast, predicates are an integral part of what would be a schema in a relational database. This gives rise to a key strength of a knowledge representation and reasoning system: The human user may directly query conclusions—without necessarily understanding or having to explicitly specify intermediate logic. This constitutes automated reasoning. 
         [0048]    Robotic Capability Ontology 
         [0049]    The robotic capability ontology defines a catalog or registry of robotic capabilities with a view towards assigning tasks in a sequence of manufacturing steps to individual robots. In order to do this, the system must firstly define what types of robots exist. The categorization hierarchies in tables 1 and 2 illustrate this. 
         [0000]    
       
         
               
             
               
               
               
               
               
             
               
               
               
               
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 Robot Classification Hierarchy 
               
               
                 Robot 
               
             
          
           
               
                 Assembly 
                 Finishing 
                 Handling 
                 ShapingForming 
                 Textile 
               
               
                   
               
             
          
           
               
                 Riveting 
                 Polishing 
                 Packaging 
                 Additive 
                 Sewing 
                 . . . 
               
               
                 Bonding 
                 Painting 
                 Conveying 
                 Subtractive 
                 Ironing 
               
               
                 . . . 
                 Brushing . . . 
                 . . . 
                 Injection Molding 
                 . . . 
               
               
                   
                   
                   
                 Fusing . . . 
               
               
                   
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
               
               
             
               
               
               
               
             
           
               
                 TABLE 2 
               
             
             
               
                   
               
               
                 Subordinate Robot Classification Hierarchy 
               
               
                 Fusing 
               
             
          
           
               
                   
                 Welding 
                 Soldering 
               
               
                   
                   
               
             
          
           
               
                   
                 Arc 
                 Soft 
                 . . . 
               
               
                   
                 Electron 
                 Hard 
               
               
                   
                 Flux 
                 Induction 
               
               
                   
                 Laser 
                 . . . 
               
               
                   
                 Mig 
               
               
                   
                 Plasma 
               
               
                   
                 Spot 
               
               
                   
                 . . . 
               
               
                   
                   
               
             
          
         
       
     
         [0050]    An elaboration of how the hierarchy from table 1 may be represented in Knowledge Interchange Format is shown in table 3. 
         [0000]    
       
         
               
             
               
             
               
               
             
               
             
               
               
             
               
             
               
               
             
               
             
               
               
             
               
             
               
               
             
               
             
               
               
             
           
               
                 TABLE 3 
               
               
                   
               
               
                 Base Robot Concepts 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 ;; Base Robot Concept 
               
               
                 ;;=============== 
               
               
                 (defconcept Robot (?r) 
               
             
          
           
               
                   
                 :documentation “The concept of ROBOT beings”) 
               
             
          
           
               
                 ;; Level 2 Robot Concepts 
               
               
                 ;;================== 
               
               
                 (defconcept AssemblyRobot (?r Robot) 
               
             
          
           
               
                   
                 :documentation “True if ?r assembles product components into a 
               
               
                   
                 product”) 
               
             
          
           
               
                 (defconcept HandlingRobot (?r Robot) 
               
             
          
           
               
                   
                 :documentation “True if ?r handles products, e.g. moving them ”) 
               
             
          
           
               
                 (defconcept FinishingRobot (?r Robot) 
               
             
          
           
               
                   
                 :documentation “True if ?r applies a finish to products, e.g. 
               
               
                   
                 polishing”) 
               
             
          
           
               
                 (defconcept ShapingFormingRobot (?r Robot) 
               
             
          
           
               
                   
                 :documentation “true if ?r is a robot that shapes or forms material”) 
               
             
          
           
               
                 (defconcept TextileRobot (?r Robot) 
               
             
          
           
               
                   
                 :documentation “true if ?r is a robot that processes textiles”) 
               
               
                   
                   
               
             
          
         
       
     
         [0051]    Likewise concept categories may be refined in the hierarchy with subcategories. 
         [0000]    
       
         
               
             
               
             
               
               
             
               
             
               
               
             
               
             
               
               
             
               
             
               
               
             
               
             
               
               
             
               
             
               
               
             
               
             
               
               
             
           
               
                 TABLE 4 
               
               
                   
               
               
                 Subordinate Robot Concepts 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 ;; Level 3 Robot Concepts 
               
               
                 ;;======================== 
               
               
                 (defconcept AdditiveRobot (?r ShapingFormingRobot) 
               
             
          
           
               
                   
                 :documentation “true if ?r is an 3D printing/additive manufacturing 
               
               
                   
                 robot”) 
               
             
          
           
               
                 (defconcept SubtractiveRobot (?r ShapingFormingRobot) 
               
             
          
           
               
                   
                 :documentation “true if ?r is a milling, drilling, machining etc. 
               
               
                   
                 robot”) 
               
             
          
           
               
                 (defconcept FusingRobot (?r ShapingFormingRobot) 
               
             
          
           
               
                   
                 :documentation “true if ?r is a robot that fuses components, e.g. 
               
               
                   
                 welding”) 
               
             
          
           
               
                 (defconcept SewingRobot (?r TextileRobot) 
               
             
          
           
               
                   
                 :documentation “true if ?r is a robot that sews textiles”) 
               
             
          
           
               
                 (defconcept PackagingRobot (?r HandlingRobot) 
               
             
          
           
               
                   
                 :documentation “true if ?r is a robot that packages materials”) 
               
             
          
           
               
                 ;; Level 4 Robot Concepts 
               
               
                 ;;======================== 
               
               
                 (defconcept WeldingRobot (?r FusingRobot) 
               
             
          
           
               
                   
                 :documentation “true if ?r is a welding robot”) 
               
             
          
           
               
                 (defconcept BondingRobot (?r FusingRobot) 
               
             
          
           
               
                   
                 :documentation “true if ?r is a robot that bonds components, e.g. 
               
               
                   
                 gluing”) 
               
               
                   
                   
               
             
          
         
       
     
         [0052]    Relations may be defined between categories. 
         [0000]    
       
         
               
             
               
             
               
               
             
               
             
               
               
             
               
             
               
               
             
               
             
               
               
             
           
               
                 TABLE 5 
               
               
                   
               
               
                 Robot Category Relations 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 ;; Robot relations 
               
               
                 ;;================ 
               
               
                 (defrelation preprocessor-of ((?r1 Robot) (?r2 Robot)) 
               
             
          
           
               
                   
                 :documentation “True if ?r1 is a preprocessor of ?r2”) 
               
             
          
           
               
                 (defrelation postprocessor-of ((?r1 Robot) (?r2 Robot)) 
               
             
          
           
               
                   
                 :documentation “True if ?r1 is a postprocessor of ?r2”) 
               
             
          
           
               
                 (defrelation can-handle-material ((?r Robot) (?m Physical-Entity)) 
               
             
          
           
               
                   
                 :documentation “True if Robot ?r can handle Physical-Entity ?m”) 
               
             
          
           
               
                 (defrelation can-handle-component ((?r Robot) (?c Component)) 
               
             
          
           
               
                   
                 :documentation “True if Robot ?r can handle Component ?c”) 
               
               
                   
                   
               
             
          
         
       
     
         [0053]    Unary relations are defined as shown below. 
         [0000]    
       
         
               
             
               
               
             
               
               
             
           
               
                 TABLE 6 
               
               
                   
               
               
                 Robot Unary Relations 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 ;; Unary relations 
               
               
                   
                 (defrelation networkable ((?r Robot)) 
               
             
          
           
               
                   
                 :documentation “True if ?r is networkable”) 
               
               
                   
                   
               
             
          
         
       
     
         [0054]    Characteristic of this method is the hierarchical specialization of concepts and their relational classification. At this point, it is possible to browse the robotic hierarchy in what is termed an “ontology browser.” Please refer to  FIG. 7 . 
         [0055]    We have shown the source code of the robotic capability ontology which is instrumental in a] demonstrating what is characteristic of the model and b] relating robotic capabilities to products and eventually to the Manufacturing Ontology. The robotic capability ontology has been described in terms of the Lisp dialect KIF. KIF may be entered through an interactive session by a human operator or client software or KIF may be read from one or more files and/or stored in one of more files. Alternate representations of the robotic capability ontology include reading aspects of the ontology from and storing it in a relational database or representing it in an object oriented class model. As described in paragraph [0029], relational databases are not optimized for inference. Likewise, the object oriented class model is not optimized for inference. Nevertheless, both object orientation and the relational database model may be used to persist at least part of the robotic capability ontology through a “Logic Object Coercion Mechanism.” Persons of ordinary skill in the art will know this also as “type casting.” To persist the robotic capability ontology in either a relational database or a UML class model, also referred to as an object oriented model, all types must be convertible to logic objects.  FIG. 13  illustrates an environment  1300 , where a relational database “Robot” entity  1310  is being adapted through a Logic Object Coercion Mechanism  1320  and ingested by a Knowledge Representation &amp; Reasoning System  1330 .  FIG. 14  illustrates an environment  1400 , where a class hierarchy expressed in UML notation is being adapted through a Logic Object Coercion Mechanism  1430  and ingested by a Knowledge Representation &amp; Reasoning System  1440 . A root class Robot  1410  is being specialized by a child class ShapingForming  1420 , which is in turn specialized by Additive  1450  and Subtractive  1460 . Classed  1410 ,  1420 ,  1450  &amp;  1460  are being promoted to logic objects by a Logic Object Coercion Mechanism  1430 . Expressing an ontology as illustrated in  FIG. 14  based on UML is less optimal than the KIF based representation for reasons explained in paragraph [0081]. Likewise, expressing an ontology as illustrated in  FIG. 13  based on relational entities is less optimal than the KIF based representation for reasons explained in paragraph [0047]. Both the UML and relational entity approach are included here for the sake of completeness with the Logic Object Coercion Mechanism offered as a means to mitigate disadvantages of the respective approaches. 
         [0000]    Tables 7 and 8 demonstrate the process described in  FIG. 13 . 
         [0000]    
       
         
               
             
               
               
             
               
               
               
             
               
               
             
               
               
               
             
               
               
               
             
               
               
             
           
               
                 TABLE 7 
               
               
                   
               
               
                 Relational Representation of Robots - 
               
               
                 Robots represented by Character Strings 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 mysql&gt; show tables; 
               
               
                   
                 +---------------------+ 
               
               
                   
                 | Tables_in_youdobot | 
               
               
                   
                 +---------------------+ 
               
             
          
           
               
                   
                 | robot 
                 | 
               
             
          
           
               
                   
                 +---------------------+ 
               
               
                   
                 1 rows in set (0.00 sec) 
               
               
                   
                 mysql&gt; describe robot; 
               
               
                   
                 +---------+-------------+------+-----+---------+-------+ 
               
               
                   
                 | Field | Type | Null | Key | Default | Extra | 
               
               
                   
                 +---------+-------------+------+-----+---------+-------+ 
               
               
                   
                 | rtype | varchar(20) | YES | | NULL | | 
               
               
                   
                 | parent | varchar(20) | YES | | NULL | | 
               
               
                   
                 +---------+-------------+------+-----+---------+-------+ 
               
               
                   
                 2 rows in set (0.01 sec) 
               
               
                   
                 mysql&gt; select * from robot, 
               
               
                   
                 +----------------+----------------+ 
               
               
                   
                 | rtype | parent | 
               
               
                   
                 +----------------+----------------+ 
               
             
          
           
               
                   
                 | Robot | 
                 | 
               
             
          
           
               
                   
                 | ShapingForming | Robot 
                 | 
               
             
          
           
               
                   
                 | Additive | ShapingForming | 
               
               
                   
                 | Subtractive | ShapingForming | 
               
               
                   
                 +----------------+----------------+ 
               
               
                   
                 4 rows in set (0.00 sec) 
               
               
                   
                 mysql&gt; 
               
               
                   
                   
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
               
             
               
               
             
               
               
             
               
               
             
               
               
             
               
               
             
               
               
             
           
               
                 TABLE 8 
               
               
                   
               
               
                 Source Code: Logic Object Coercion 
               
               
                 Mechanism - KIF Type Coercion 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 PL-USER |= (defdb mydb 
               
             
          
           
               
                   
                 :sql-database true 
               
               
                   
                 :jdbc-connection-string 
               
             
          
           
               
                   
                 ″jdbc:mysql://localhost:3306/youdobot?user=u&amp;password=p″) 
               
               
                   
                 |i|MYDB 
               
               
                   
                 PL-USER |= (retrieve 4 (rdbms/query-database mydb 
               
             
          
           
               
                   
                 ″select rtype from robot″ ?r)) 
               
             
          
           
               
                   
                 There are 4 solutions so far: 
               
               
                   
                  #1: ?R=|Robot| 
               
               
                   
                  #2: ?R=|ShapingForming| 
               
               
                   
                  #3: ?R=|Additive| 
               
               
                   
                  #4: ?R=|Subtractive| 
               
               
                   
                 ;; 
               
               
                   
                 ;; Note that KIF has cast the character strings from the 
               
               
                   
                 ;; database to logic objects, denoted by the vertical bars. 
               
               
                   
                 ;; This is called coercion in KIF. 
               
               
                   
                 ;; We can turn coercion off. 
               
               
                   
                 PL-USER |= (retrieve 4 (?r string) 
               
             
          
           
               
                   
                 (rdbms/query-database mydb 
               
               
                   
                 ″select rtype from robot″ ?r)) 
               
             
          
           
               
                   
                 There are 4 solutions so far: 
               
               
                   
                  #1: ?R=”Robot” 
               
               
                   
                  #2: ?R=”ShapingForming” 
               
               
                   
                  #3: ?R=”Additive” 
               
               
                   
                  #4: ?R=”Subtractive” 
               
               
                   
                 ;; Note that KIF has now retained the native character 
               
               
                   
                 ;; string types, denoted by the double quotes surrounding 
               
               
                   
                 ;; ”Robot”, ”ShapingForming”, ”Additive”, and ”Subtractive”. 
               
               
                   
                   
               
             
          
         
       
     
         [0056]    Manufacturing Ontology 
         [0057]    The Manufacturing Ontology defines concepts underlying products and their manufacture. Like the Robotic Capability Ontology, the Manufacturing Ontology utilizes the Knowledge Interchange Format, KIF, but its specification encompasses concepts, relations, functions as well as full predicate calculus. 
         [0000]    
       
         
               
             
               
             
           
               
                 TABLE 9 
               
               
                   
               
               
                 Manufacturable Product Concepts 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 ;; Manufacturable Product Concepts 
               
               
                 ;;================================= 
               
               
                 (defconcept Entity (?e) 
               
               
                     :documentation “The concept of an unqualified thing/matter”) 
               
               
                 (defconcept Physical-Entity (?e Entity) 
               
               
                     :documentation “A physical entity is a quantifiable entity, e.g, one 
               
               
                     with size.”) 
               
               
                 (defconcept Non-Physical-Entity (?e Entity) 
               
               
                     :documentation “A non physical entity without mass; for example, 
               
               
                     a polish.”) 
               
               
                 (defrelation part-of ((?x Physical-Entity) (?y Physical-Entity)) 
               
               
                     :documentation “True if ?x is a part of ?y.”) 
               
               
                 (defrelation aggregates ((?x Physical-Entity) (?y Physical-Entity)) 
               
               
                     :documentation “True if ?x is made up of ?y.”) 
               
               
                   
               
             
          
         
       
     
         [0058]    Table 9 defines entities and physical entities. We also establish that physical entities may be related by being part of or aggregating one another. It has yet to be defined what that means. For this we introduce rules of implication. The first rule combines mathematical quantification (forall) with implication (=&gt;). 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 10 
               
               
                   
               
               
                 Part-of Concept 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 ;; Define the part-of concept 
               
               
                   
                 ;;================================= 
               
               
                   
                 ;; If x is a part of y then y aggregates x 
               
               
                   
                 ;; Note that part-of is not transitive. 
               
               
                   
                 (assert (forall ((?x Physical-Entity) (?y Physical-Entity)) 
               
               
                   
                    (=&gt; (part-of ?x ?y) 
               
               
                   
                     (aggregates ?y ?x)))) 
               
               
                   
               
             
          
         
       
     
         [0059]    What the rule in table 10 says is that any one (x) which is part of another (y), the other part (y) aggregates the one (x). Therefore “part-of” is the inverse of “aggregates” The implication is non-transitive. But aggregates can be transitive. That is how composites are created. 
         [0000]    
       
         
               
             
               
             
           
               
                 TABLE 11 
               
               
                   
               
               
                 Transitive Concepts 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 ;; Transitivity: If {grave over ( )}?x′ aggregates {grave over ( )}?y′ and {grave over ( )}?y′ aggregates {grave over ( )}?z′ then {grave over ( )}?x′ 
               
               
                 aggregates {grave over ( )}?z′. 
               
               
                 ;; This is the chain rule of implication used to construct recursion. This 
               
               
                 ;; rule is recursive, since its consequent (or head) defines an ‘aggregates’ 
               
               
                 ;; relationship by recursively referencing ‘aggregates’ in its antecedent 
               
               
                 (or tail). 
               
               
                 (assert (forall ((?x Physical-Entity) (?z Physical-Entity)) 
               
               
                    (=&gt; (exists (?y Physical-Entity) 
               
               
                      (and (aggregates ?x ?y) 
               
               
                       (aggregates ?y ?z))) 
               
               
                    (aggregates ?x ?z)))) 
               
               
                   
               
             
          
         
       
     
         [0060]    Above we are formulating a rule based on universal quantification (forall), implication (=&gt;) and additionally existential quantification (exists). What we are saying is that for all x and z where a y exists such that x aggregates y and y aggregates z then x also aggregates z. This means no direct relationship between x and z needs to be specified. It is now inferable—and this inference will work no matter how many intermediate aggregates connect x and z, because the rule is, in computer science terms, “recursive.” 
         [0061]    We now proceed to distinguish between part-of and Part since at any point in time a Part, while intended for a whole, may not be part-of a whole. Further we define made-of to be single valued (axiom single-valued). This is because we will define composites separately. See tables 12 and 13. 
         [0000]    
       
         
               
             
               
             
           
               
                 TABLE 12 
               
               
                   
               
               
                 Made-of Concept 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 ;; The concept of a part: We try to distinguish this from the relationship 
               
               
                 ;; part-of in that a part may not at any point in time be part of anything. 
               
               
                 ;; We call things parts that are intended to function integrated in something 
               
               
                 ;; else yet spend part of their life cycle not integrated in that something 
               
               
                 ;; else. For example, a light bulb fulfills a purpose in a lamp, but if spare 
               
               
                 ;; may exists outside of that context. Hence a part may not be “part of.” 
               
               
                 (defconcept Part (?part Physical-Entity) 
               
               
                    :documentation “A part is a thing that fulfills a greater purpose 
               
               
                     partially”) 
               
               
                 (defrelation made-of ((?p Part) (?m Physical-Entity)) 
               
               
                     :documentation “True if Part ?p is made of Physical-Entity ?m.” 
               
               
                     :axioms ((single-valued made-of))) 
               
               
                   
               
             
          
         
       
     
         [0062]    In table 13 we define composites to be wholes composed of more than one Part. Firstly, we define the concept of a Composite to be a specialization of Part. Secondly we define a rule, which asserts that when the cardinality of the aggregates relation involving any x is at least two (range-cardinality-lower-bound), then x is a Composite. Because Composites are Parts, x must also be a Part. The specification is shorter than it&#39;s explanation. 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 13 
               
               
                   
               
               
                 Composite Concept 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 ;; Concept of a composite: 
               
               
                   
                 ;; must have at least two parts 
               
               
                   
                 (defconcept Composite (?p Part)) 
               
               
                   
                 (assert (forall ?x 
               
               
                   
                     (=&gt; (range-cardinality-lower-hound aggregates ?x 2) 
               
               
                   
                     (Composite ?x)))) 
               
               
                   
               
             
          
         
       
     
         [0063]    At last we are equipped to define the Component that we saw in the earlier section. It is hoped that the definition is intuitive. Please refer to the comments in the code. 
         [0000]    
       
         
               
             
               
             
           
               
                 TABLE 14 
               
               
                   
               
               
                 Component Concept 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 ;; A Component is a part such that there exists a whole that aggregates it 
               
               
                 ;; in a way that there are at least two parts aggregated by that whole. 
               
               
                 (defconcept Component (?p Part) 
               
               
                    :documentation “A part of something; not the whole; 
               
               
                    having two or more Physical-Entities or other Components” 
               
               
                    :&lt;=&gt; (exists (?whole) 
               
               
                       (and (aggregates ?whole ?p) 
               
               
                        (Composite ?whole)))) 
               
               
                   
               
             
          
         
       
     
         [0064]    It is necessary to link the Manufacturing Ontology with the Robotic Capability Ontology in order to support a use case of automated reasoning assisted product design. Individual robots have specific capabilities. For example, we defined assembly robots to include bonding, and fusing robots to include welding. These are methods to “join” parts, but we have yet to introduce that concept. 
         [0065]    In table 15 we define the concept Join in terms of a set and define a function join-using that maps any two Components to a Join. 
         [0000]    
       
         
               
             
               
             
           
               
                 TABLE 15 
               
               
                   
               
               
                 Join Concept 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 (defconcept Join (?j) 
               
               
                     :&lt;=&gt; (member-of ?j (setof 
               
               
                          WELD 
               
               
                          GLUE 
               
               
                          NAIL 
               
               
                          STAPLE 
               
               
                          SCREW 
               
               
                          ARTICULATED-HINGE 
               
               
                          STITCH 
               
               
                          RIVET 
               
               
                          ))) 
               
               
                 (deffunction join-using ((?x Component) (?y Component)) :-&gt;(?j Join)) 
               
               
                   
               
             
          
         
       
     
         [0066]    We are now equipped to define the requires-materials-joined-by relation. Consider the case of a robot that can glue to support a particular Join, but which cannot glue any arbitrary two materials. Perhaps only wood can be glued onto wood, but not onto metal. This is one example of enforcing constraints. We define requires-materials-joined-by as shown in table 16. 
         [0000]    
       
         
               
             
               
             
           
               
                 TABLE 16 
               
               
                   
               
               
                 Requires Materials Joined By Relation 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 (defrelation requires-materials-joined-by ((?x Part) 
               
               
                             (?m1 Physical-Entity) 
               
               
                             (?m2 Physical-Entity) 
               
               
                             (?j Join))) 
               
               
                 (assert (forall ((?whole Part) (?m1 Physical-Entity) (?m2 Physical-Entity) 
               
               
                 (?j Join)) 
               
               
                     (=&gt; (exists ((?c1 Component) (?c2 Component)) 
               
               
                        (and (aggregates ?whole ?c1) 
               
               
                          (aggregates ?whole ?c2) 
               
               
                          (made-of ?c1 ?m1) 
               
               
                          (made-of ?c2 ?m2) 
               
               
                          (= (join-using ?c1 ?c2) ?j))) 
               
               
                       (requires-materials-joined-by ?whole 
               
               
                               ?m1 
               
               
                               ?m2 
               
               
                               ?j)))) 
               
               
                   
               
             
          
         
       
     
         [0067]    What the above says is define a relation requires-materials-joined-by. Then assert that for all wholes and any two physical entities (m1 &amp; m2) and a join (j) the following must hold true: Where two components exist, which are aggregated by the whole and are made of m1 &amp; m2 and where the join function taking the components as arguments produces j, then what is implied is that the relation requires-materials-joined-by is true. 
         [0068]    In the introduction we said that inference rules could be applied directly within data definitions—the “schema.” This is one example of that method. 
         [0069]    This method becomes useful in that the definition of a product, we will see this later, as a composite forms what computer scientists call a “graph”. A query of this product using requires-materials-joined-by, then mimics what the computer science discipline of functional programming calls a “destructuring pattern match.” The pattern match “parses” the product structure, the graph, and produces results satisfying the pattern, here a relation. Unlike in a conventional programming language, no additional code needs to be written to actively perform this pattern match within a function. Rather, the conclusions of the computation may be queried directly. 
         [0070]    In this way, we are able to unify aspects of object orientation (hierarchical specialization), functional programming (pattern-match-style graph-destructuring), relational database concepts (relations) and rules of logic (predicate calculus) into one holistic model that enables automated reasoning. As described in paragraph [0037] with respect to the Robotic Capability Ontology, particular aspects of the Manufacturing Ontology may likewise be persisted (stored in and/or read form) using a class model or a relational model based representation. 
         [0071]    Materials are defined as shown in table 17. 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 17 
               
               
                   
               
               
                 Example Materials Defined 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 ;;=================== 
               
               
                   
                 ;; Materials List 
               
               
                   
                 ;;=================== 
               
               
                   
                 (assert (Physical-Entity plastic)) 
               
               
                   
                 (assert (Physical-Entity wood)) 
               
               
                   
                 (assert (Physical-Entity paper)) 
               
               
                   
                 (assert (Physical-Entity cardboard)) 
               
               
                   
                 (assert (Physical-Entity steel)) 
               
               
                   
                 (assert (Physical-Entity glass)) 
               
               
                   
                 (assert (Physical-Entity rubber) 
               
               
                   
                 (assert (Physical-Entity brass)) 
               
               
                   
               
             
          
         
       
     
         [0072]    We are now ready to link robots to materials they can handle and link assembly robots, in particular, to join methods they support. 
         [0073]    Product definitions are now possible in terms of the ontology we have established. In this method, products form what is called a “directed graph” defining their components, any materials used and means of assembly. 
         [0074]    We begin by asserting materials, plastic and wood as well as defining Parts case and lid towards construction of a box. 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 18 
               
               
                   
               
               
                 Example Product Materials and Associated Parts 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 (assert (Physical-Entity plastic)) 
               
               
                   
                 (assert (Physical-Entity wood)) 
               
               
                   
                 (assert (Part case)) 
               
               
                   
                 (assert (Part lid)) 
               
               
                   
               
             
          
         
       
     
         [0075]    Now we define the materials of which case and lid are made. 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 19 
               
               
                   
               
               
                 Example Product Materials Continued 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 (assert (made-of case plastic)) 
               
               
                   
                 (assert (made-of lid wood)) 
               
               
                   
               
             
          
         
       
     
         [0076]    Next we define the box. This builds our product as a directed graph. 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 20 
               
               
                   
               
               
                 Example Product Definition as Directed Graph 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 (assert (Part box)) 
               
               
                   
                 (assert (part-of lid box)) 
               
               
                   
                 (assert (part-of case box)) 
               
               
                   
               
             
          
         
       
     
         [0077]    Lastly, for our limited example, we define the method of construction. 
         [0000]    
       
         
               
             
           
               
                 TABLE 21 
               
               
                   
               
               
                 Example Product Method of Construction 
               
               
                   
               
             
             
               
                 (assert (= (join-using case lid) ARTICULATED-HINGE)) 
               
               
                   
               
             
          
         
       
     
         [0078]    An articulated hinge joins case and lid. 
         [0079]    We may now browse our knowledge base in an ontology browser. The illustration below shows navigating to the Part entity and browsing box, case and lid. The “Propositions for Box” pane shows what a box is. It is a part. Case and lid are its parts. Please refer to  FIG. 8 . 
         [0080]    Likewise we may query what is part of a box programmatically: 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 22 
               
               
                   
               
               
                 Querying Product Components 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 STELLA&gt; (retrieve all (?x) (aggregates box ?x)) 
               
               
                   
                 ;There are 2 solutions: 
               
               
                   
                 ;#1: ?X=CASE 
               
               
                   
                 ;#2: ?X=LID 
               
               
                   
               
             
          
         
       
     
         [0081]    Automated Reasoning 
         [0082]    We can also ask the system to reason about our box. One thing we have not explicitly stated is whether a box is a Composite. But recall that our definition of Composite was an aggregate with 2 or more parts. If one needs to know, one can simply issue the (why) command. 
         [0000]    
       
         
               
             
               
             
           
               
                 TABLE 23 
               
               
                   
               
               
                 Explaining Propositions 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 STELLA&gt; (ask (Composite box)) 
               
               
                 TRUE 
               
               
                 STELLA&gt; (why) 
               
               
                 1 (COMPOSITE BOX) 
               
               
                  follows by Modus Ponens 
               
               
                  with substitution {?x/BOX} 
               
               
                  since 1.1 ! (FORALL (?x) 
               
               
                      (&lt;= (COMPOSITE ?x) 
               
               
                       (RANGE-CARDINALITY-LOWER-BOUND 
               
               
                  AGGREGATES ?x 2))) 
               
               
                  and 1.2 (RANGE-CARDINALITY-LOWER-BOUND 
               
               
                 AGGREGATES BOX 2) 1.2 (RANGE-CARDINALITY-LOWER- 
               
               
                 BOUND AGGREGATES BOX 2) 
               
               
                  follows by Modus Ponens 
               
               
                  with substitution {?v05/2, ?lb/2, ?i/BOX, ?r/AGGREGATES} 
               
               
                  since 1.2.1 ! (FORALL (?r ?i ?lb) 
               
               
                      (&lt;= (RANGE-CARDINALITY-LOWER-BOUND ?r ?i ?lb) 
               
               
                       (EXISTS (?v05) 
               
               
                        (AND (BOUND-VARIABLES ?r ?i ?lb) 
               
               
                         (= (RANGE-MIN-CARDINALITY ?r ?i) ?v05) 
               
               
                         (=&lt; ?lb ?v05))))) 
               
               
                  and 1.2.2 (BOUND-VARIABLES AGGREGATES BOX 2) 
               
               
                  and 1.2.3 (= (RANGE-MIN-CARDINALITY AGGREGATES BOX) 
               
               
                  2) and 1.2.4 (=&lt; 2 2) 
               
               
                 ... 
               
               
                   
               
             
          
         
       
     
         [0083]    As the excerpt shows, we may even ask the system to explain why it thinks that a box is a Composite. The astute mathematician will note that part of the proof has been elided. Enough of the full proof has been shown to demonstrate the reasoning capabilities of the system. 
         [0084]    Most importantly, we want to know how to build our box. What materials will need to be joined onto what others and how? 
         [0000]    
       
         
               
             
               
               
             
               
               
             
               
               
             
           
               
                 TABLE 24 
               
               
                   
               
               
                 Querying Product Materials and Means of Construction 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 STELLA&gt; (retrieve all (?m1 ?m2 ?j) 
               
             
          
           
               
                   
                 (requires-materials-joined-by box ?m1 ?m2 ?j)) 
               
             
          
           
               
                   
                 There is 1 solution: 
               
               
                   
                 #1: ?M1=PLASTIC, ?M2=WOOD, ?J=ARTICULATED-HINGE 
               
               
                   
                   
               
             
          
         
       
     
         [0085]    The “retrieve all” command has triggered the parsing of the product graph for box and all relations satisfying the requires-materials-joined-by predicate have been identified. Free variables m1, m2, j have been filled in. Of interest is the “SQL style” declarative nature of this command. Yet there are no data tables. Instead, a graph based data structure has been parsed and processed—a task familiar to computer programmers as procedural rather than declarative. Traditionally this is where computer programmers spend and lose their development time: Data structures are defined—declaratively. Then procedural algorithms are constructed to parse and process them. Finally, the data model changes and the algorithms must be re-written—or vise versa. Likewise, in a data driven model, such as a relational database, stored procedures assume the role of the algorithm. This disjoin requires a human domain expert to use understanding of the data model &amp; algorithm. This understanding is codified in procedures, which themselves cannot be reasoned about algebraically. The esteemed mathematician E. W. Dijkstra called this “operational reasoning” and considered it “to be one of the main causes of the persistence of the software crisis.” The technology described in the present disclosure solves this problem. 
         [0086]    Characteristic of the implementation we have presented here is that concepts are described in terms of predicate calculus and that relational and functional concepts are integrated in a way that the system can reason about itself. Computations are produced without explicitly traversing and parsing the data model. 
         [0087]    Deducing Manufacturing Plans 
         [0088]    Robot definitions allow connecting knowledge of how to build products to deducing manufacturing plans. We begin by defining robots. 
         [0000]    
       
         
               
             
               
             
           
               
                 TABLE 25 
               
               
                   
               
               
                 Example Robot Definitions 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 ;;========================= 
               
               
                 ;; Sample Robot Definitions 
               
               
                 ;;========================= 
               
               
                 ;; A 3D Printer, not otherwise qualified 
               
               
                 (assert (Robot 3dprinter)) 
               
               
                 ;; A box assembly robot that can handle wood, plastic and steel 
               
               
                 ;; using hinges, rivets and staples 
               
               
                 (assert (AssemblyRobot boxmakerbot)) 
               
               
                 (assert (can-handle-material boxmakerbot wood)) 
               
               
                 (assert (can-handle-material boxmakerbot plastic)) 
               
               
                 (assert (can-handle-material boxmakerbot steel)) 
               
               
                 (assert (can-join-using boxmakerbot ARTICULATED-HINGE)) 
               
               
                 (assert (can-join-using boxmakerbot RIVET)) 
               
               
                 (assert (can-join-using boxmakerbot STAPLE)) 
               
               
                 ;; WhoIsBaxter 
               
               
                 (assert (HandlingRobot whoisbaxter)) ; WhoIsBaxter is versatile 
               
               
                 (assert (FinishingRobot whoisbaxter)) ; Illustration given to show that 
               
               
                 (assert (AssemblyRobot whoisbaxter)) ; multiple categorizations are 
               
               
                 allowed 
               
               
                 ;; A fusing robot that can glue components 
               
               
                 (assert (FusingRobot stickerbot)) 
               
               
                 (assert (can-handle-material stickerbot wood)) 
               
               
                 (assert (can-handle-material stickerbot brass)) 
               
               
                 (assert (can-join-using stickerbot GLUE)) 
               
               
                   
               
             
          
         
       
     
         [0089]    We have already seen the relation requires-materials-joined-by. We modify this slightly to accommodate robots and define the requires-materials-joined-by-robot. 
         [0000]    
       
         
               
             
               
               
             
               
               
             
               
               
             
               
               
             
               
               
             
               
               
             
               
               
             
               
               
             
           
               
                 TABLE 26 
               
               
                   
               
               
                 Mapping Means of Construction onto Robots 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 ;; Define requires-materials-joined-by-robot relation 
               
               
                   
                 (defrelation requires-materials-joined-by-robot ((?x Part) 
               
             
          
           
               
                   
                 (?m1 Physical-Entity) 
               
               
                   
                 (?m2 Physical-Entity) 
               
               
                   
                 (?j Join) 
               
               
                   
                 (?r Robot))) 
               
             
          
           
               
                   
                 (assert (forall ((?whole Part) 
               
             
          
           
               
                   
                 (?m1 Physical-Entity) 
               
               
                   
                 (?m2 Physical-Entity) 
               
               
                   
                 (?j Join) 
               
               
                   
                 (?r Robot)) 
               
               
                   
                 (=&gt; (exists ((?c1 Component) (?c2 Component)) 
               
             
          
           
               
                   
                 (and (aggregates ?whole ?c1) 
               
             
          
           
               
                   
                 (aggregates ?whole ?c2) 
               
               
                   
                 (made-of ?c1 ?m1) 
               
               
                   
                 (made-of ?c2 ?m2) 
               
               
                   
                 (= (join-using ?c1 ?c2) ?j) 
               
               
                   
                 (can-handle-material ?r ?m1) 
               
               
                   
                 (can-handle-material ?r ?m2) 
               
               
                   
                 (can-join-using ?r ?j) 
               
               
                   
                 )) 
               
             
          
           
               
                   
                 (requires-materials-joined-by-robot ?whole 
               
             
          
           
               
                   
                 ?m1 
               
               
                   
                 ?m2 
               
               
                   
                 ?j 
               
               
                   
                 ?r)))) 
               
               
                   
                   
               
             
          
         
       
     
         [0090]    Feasibility Analysis 
         [0091]    Before we asked what materials will need to be joined onto what others and how? Now it is possible to ask what robot can provide this service to establish if the design is feasible. If there are no solutions, the design is not feasible. If the query finds solutions, the design is feasible. 
         [0000]    
       
         
               
             
               
               
             
               
               
             
               
               
             
               
               
               
             
           
               
                 TABLE 27 
               
               
                   
               
               
                 Querying Robots Required to Construct a Product 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 STELLA&gt; (retrieve all (?m1 ?m2 ?j ?r) 
               
             
          
           
               
                   
                  (requires-materials-joined-by-robot box ?m1 ?m2 ?j ?r)) 
               
             
          
           
               
                   
                 There is 1 solution: 
               
             
          
           
               
                   
                 #1: 
                 ?M1=PLASTIC, 
               
               
                   
                   
                 ?M2=WOOD, 
               
               
                   
                   
                 ?J=ARTICULATED-HINGE, 
               
               
                   
                   
                 ?R=BOXMAKERBOT 
               
               
                   
                   
               
             
          
         
       
     
         [0092]    Our design is feasible. We will require the “boxmakerbot” robot. It is worth noting that this automated exploration of feasibility supports not only queries by human operators, but also permits software assisted product design. Thus, where traditionally supply models utilized a strategy bringing branded products to consumers, the method presented here is designed to support a pull supply chain model that sees bespoke products composed from capabilities. Manufacturers become vendors of capabilities. See overleaf for more manufacturing plans. 
         [0093]    The next paragraph is intended to show how compositional the system is and to demonstrate the concept of a manufacturing plan. A manufacturing plan details what robots are needed to complete a product requiring multiple manufacturing steps—and possibly the order of steps. The steps below illustrate an elaboration of the box design.
       Define a plaque;   Make it from brass.   Then add it to the box and   glue it to the lid.       
 
         [0098]    Shown below is our definition. 
         [0000]    Note how the “code” models the structure of the English description that we just gave. 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 28 
               
               
                   
               
               
                 Extending the Example Product Definition 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 (assert (Part plaque)) 
               
               
                   
                 (assert (made-of plaque brass)) 
               
               
                   
                 (assert (part-of plaque box)) 
               
               
                   
                 (assert (= (join-using lid plaque) GLUE)) 
               
               
                   
                   
               
             
          
         
       
     
         [0099]    Finally, we ask the system to produce a manufacturing plan. 
         [0000]    
       
         
               
             
               
             
               
               
             
               
             
               
               
             
           
               
                 TABLE 29 
               
               
                   
               
               
                 Computing a Manufacturing Plan 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 STELLA&gt; (retrieve all (?m1 ?m2 ?j ?r) 
               
             
          
           
               
                   
                  (requires-materials-joined-by-robot box ?m1 ?m2 ?j ?r)) 
               
             
          
           
               
                 There are 2 solutions: 
               
             
          
           
               
                 #1: 
                 ?M1=WOOD, ?M2=BRASS, ?J=GLUE, ?R=STICKERBOT 
               
               
                 #2: 
                 ?M1=PLASTIC, 
               
               
                   
                 ?M2=WOOD, 
               
               
                   
                 ?J=ARTICULATED-HINGE, 
               
               
                   
                 ?R=BOXMAKERBOT 
               
               
                   
               
             
          
         
       
     
         [0100]    Non Physical Entities 
         [0101]    We can easily extend the model to add processing steps that include non-physical entities. We never mentioned why materials are modelled as physical entities—to allow them to be composed transparently with manufacturing steps involving non physical entities. For example we might imagine that the brass plate would be “composed” with a polish. In that case, the polish is a non physical entity (one without mass) that is applied to the brass plate. We define as follows: 
         [0000]    
       
         
               
             
               
             
               
               
             
               
             
           
               
                 TABLE 30 
               
               
                   
               
               
                 Non Physical Entities 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 (defconcept Non-Physical-Entity (?e Entity) 
               
             
          
           
               
                   
                 :documentation “A non physical entity without mass; for example a 
               
               
                   
                 polish.”) 
               
             
          
           
               
                 (assert (Non-Physical-Entity polish)) 
               
               
                   
               
             
          
         
       
     
         [0102]    Quantitative Reasoning &amp; Satisficing 
         [0103]    Discrete reasoning systems are powerful, but are plagued by the fact that real world data is often quantitative rather than qualitative. In our previous example, an entity could be either physical or non-physical. This is a discrete quality ascribed to entities in our ontology. Much of our world is described differently. Weather can be good or bad. This too is qualitative. But the goodness or badness of weather exists on a continuum. Temperature is measured in degrees. Precipitation is measured in terms of volume. Sunshine is measured in hours per day. This continuous data is also termed “quantitative.” For any day we may have some of this information—but not necessarily all—and our information may be imprecise or contradictory. The way humans arrive at actionable conclusions is termed “satisficing,” This process discerns what is statistically relevant in economic terms from what is not. To do this we have to combine qualitative reasoning with quantitative reasoning. 
         [0104]    The method described here integrates quantitative logic with the discrete logic of predicate calculus to facilitate decision-making based on incomplete and continuous data. 
         [0105]    The example below uses the concept of Meantime Between Failure (MBF) of products and parts as an illustration. 
         [0106]    We begin by defining a statistically meaningful sample population—an excerpt is shown in table 31. 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 31 
               
               
                   
               
               
                 Sample Population 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 ;; A parts sample population 
               
               
                   
                 (assert (Part axle)) 
               
               
                   
                 (assert (Part bearing)) 
               
               
                   
                 (assert (Part belt)) 
               
               
                   
                 (assert (Part box)) 
               
               
                   
                 (assert (Part bracket)) 
               
               
                   
                 (assert (Part buckle)) 
               
               
                   
                 (assert (Part bulb)) 
               
               
                   
                 (assert (Part bushing)) 
               
               
                   
                 (assert (Part button)) 
               
               
                   
                 (assert (Part canvas)) 
               
               
                   
                 (assert (Part cart)) 
               
               
                   
                 (assert (Part case)) 
               
               
                   
                 (assert (Part chain)) 
               
               
                   
                 (assert (Part cylinder)) 
               
               
                   
                 (assert (Part drawer)) 
               
               
                   
                 (assert (Part driveshaft)) 
               
               
                   
                 ... 
               
               
                   
                   
               
             
          
         
       
     
         [0107]    We also define the concepts of Meantime Between Failure (MBF) and “HeavyDuty” construction. 
         [0000]    
       
         
               
             
               
             
               
               
             
               
             
               
               
             
           
               
                 TABLE 32 
               
               
                   
               
               
                 Meantime Between Failure Defined 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 ; Define function Meantime Between Failure 
               
               
                 (deffunction mbf ((?p Part)) :−&gt; (?i float) 
               
             
          
           
               
                   
                 :documentation “Meantime between failure of a part in months”) 
               
             
          
           
               
                 ; Unary relation HeavyDuty 
               
               
                 (defrelation heavyduty ((?p Part)) 
               
             
          
           
               
                   
                 :documentation “Parts advertisied by manufacturer as heavy duty”) 
               
               
                   
                   
               
             
          
         
       
     
         [0108]    Meantime Between Failure (MBF) in our model is TOTAL failure. Therefore a dresser with drawers where each drawer has a handle is not deemed to have failed in total if one handle breaks. A handle on its own that breaks suffers total failure, as it is no longer usable. Therefore a composite with multiple components will likely have a longer MBF than its components alone. We anticipate manufacturer advertisings to have a mixed impact on MBF. 
         [0109]    An excerpt of sample data for parts is shown below. 
         [0000]    Note that this data is purely fictional and for illustration purposes only. 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 33 
               
               
                   
               
               
                 Meantime Between Failure Sample Data 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 (assert (mbf axle 12.3)) 
               
               
                   
                 (assert (mbf bearing 46.0)) 
               
               
                   
                 (assert (mbf belt 19.2)) 
               
               
                   
                 (assert (mbf box 48.0)) 
               
               
                   
                 (assert (mbf bracket 13.7)) 
               
               
                   
                 (assert (mbf buckle 12.4)) 
               
               
                   
                 (assert (mbf bulb 25.2)) 
               
               
                   
                 (assert (mbf bushing 10.9)) 
               
               
                   
                 (assert (mbf button 19.8)) 
               
               
                   
                 (assert (mbf canvas 12.3)) 
               
               
                   
                 (assert (mbf cart 77.4)) 
               
               
                   
                 (assert (mbf case 23.0)) 
               
               
                   
                 (assert (mbf chain 8.3)) 
               
               
                   
                 (assert (mbf cylinder 12.5)) 
               
               
                   
                 (assert (mbf drawer 25.2)) 
               
               
                   
                 (assert (mbf driveshaft 14.6)) 
               
               
                   
                 ... 
               
               
                   
                   
               
             
          
         
       
     
         [0110]    We will assume that composite product definitions are entered in the model as shown before. 
         [0111]    We may now proceed to reason about parts for which we are missing Meantime Between Failure data. For this we will use an integrated neural network. Our neural network will be trained on MBF sample data and the propositional properties of Parts. For our purposes these will be the “part-of” and the “heavyduty” relations. 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 34 
               
               
                   
               
               
                 Machine Learning Configuration 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 ;; Define Neural Network 
               
               
                   
                 (set-error-print-cycle 1) 
               
               
                   
                 (set-partial-match-mode :nn) 
               
               
                   
                 (set-neural-network-training-method :BACKPROP) 
               
               
                   
                 (set-learning-rate 0.2) 
               
               
                   
                 (set-momentum-term 0.8) 
               
               
                   
                 ;; Define regression module which will learn to 
               
               
                   
                 ;; predict the age of a person based on a person&#39;s structural 
               
               
                   
                 ;; properties. 50 training cycles. 
               
               
                   
                 (structured-neural-network-regression part mbf 50) 
               
               
                   
                   
               
             
          
         
       
     
         [0112]    We define a new part and enquire about its predicted MBF. 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 35 
               
               
                   
               
               
                 Regression Based Prediction 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 STELLA&gt; (assert (Part unknownPart)) 
               
               
                   
                 STELLA&gt; (approximate unknownPart Mbf) 
               
               
                   
                 ANSWER&gt; 17.976807290601176 
               
               
                   
                   
               
             
          
         
       
     
         [0113]    Based on everything we know about parts in general, just knowing that unknownPart is a Part yields a prediction of 17.97 months meantime between failure. This is essentially in line with the central tendency for MBF in our sample data. How would this prediction change if we knew that its manufacturer classed the unknownPart as heavy duty? The system permits a direct and ad-hoc analysis of this question. 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 36 
               
               
                   
               
               
                 Machine Learning Feature Selection 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 STELLA&gt; (assert (HeavyDuty unknownPart)) 
               
               
                   
                 |P|(HEAVYDUTY UNKNOWNPART) 
               
               
                   
                 STELLA&gt; (approximate unknownPart Mbf) 
               
               
                   
                 ANSWER&gt; 18.89186548264741 
               
               
                   
                   
               
             
          
         
       
     
         [0114]    Apparently the impact is negligible—for this data model. This mechanism allows ad-hoc selection of features that are relevant to our model—traditionally a difficult task. 
         [0115]    Deductive Quantitative Reasoning 
         [0116]    The following paragraphs explore how being a “Composite” impacts the MBF prediction. Three components are created and designated parts of the “unknownPart,” thus rendering “unknownPart” a composite. How is the MBF prediction affected? 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 37 
               
               
                   
               
               
                 Machine Learning Feature Selection Continued 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 STELLA&gt; (assert (Part componentA)) 
               
               
                   
                 STELLA&gt; (assert (Part componentB)) 
               
               
                   
                 STELLA&gt; (assert (Part componentC)) 
               
               
                   
                 STELLA&gt; (assert (part-of componentA unknownPart)) 
               
               
                   
                 STELLA&gt; (assert (part-of componentB unknownPart)) 
               
               
                   
                 STELLA&gt; (assert (part-of componentC unknownPart)) 
               
               
                   
                 STELLA&gt; (approximate unknownPart Mbf) 
               
               
                   
                 ANSWER&gt; 48.83258616830254 
               
               
                   
                   
               
             
          
         
       
     
         [0117]    The predicted part-of relation is transitive. It stands to reason that in a composite with a breadth of MBF has increased substantially reflecting the composite structure. What happens if we have a chain of components with subcomponents? Recall that the components (e.g. a dresser with many drawers) improves its Meantime Between Failure through many components, but that the inverse is true for a long and narrow chain of components. Any link in the chain can induce failure. This is modelled as follows. 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 38 
               
               
                   
               
               
                 Machine Learning Feature Inference 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 STELLA&gt; (assert (Part componentAA)) 
               
               
                   
                 STELLA&gt; (assert (Part componentBB)) 
               
               
                   
                 STELLA&gt; (assert (Part componentCC)) 
               
               
                   
                 STELLA&gt; (assert (part-of componentAA componentA)) 
               
               
                   
                 STELLA&gt; (assert (part-of componentBB componentB)) 
               
               
                   
                 STELLA&gt; (assert (part-of componentCC componentC)) 
               
               
                   
                 STELLA&gt; (approximate unknownPart Mbf) 
               
               
                   
                 ANSWER&gt; 31.72638230248698 
               
               
                   
                   
               
             
          
         
       
     
         [0118]    The outcome is as we predicted. A chain of subcomponents adversely affects reliability. But note how we said nothing new about “unknownPart” at all. The system has inferred the result through reasoning about the transitive relation “part-of” alone. Also note once more how the analysis is both ad-hoc and immediate. In a mainstream model of computation there would typically have been inter-process communication between the computation and a database or a statistical analysis system, followed by the return of the analytical results for evaluation. Here results are immediate because analysis, database and computation are integrated. 
         [0119]    Visual Modeling 
         [0120]    Visual Programming assists non-technical users in approaching complex domain expertise. The technique is frequently used in elementary schools to introduce children to computer programming: Children model sprites in an editor that is analogous to a game simulator. This paradigm also resonates with modeling of toy robotics and their movements through a scene. The approach presented here extends the concept of sprites moving through a scene to the concept of parts moving through a supply chain and products being described in terms of the steps of their manufacture. Block-oriented lexical constructs are mapped to the Lisp based syntax and semantics in PowerLoom and its Knowledge Interchange Format KIF, allowing only blocks to be connected that result in syntactically correct constructs. Please refer to  FIG. 9  for an example of a visual model of a product specified in terms of the manufacturing ontology described in this disclosure. 
         [0121]      FIG. 10  is a block diagram describing geometric syntax verification in visual programming  1000 . Visual programming centers on the visual manipulation of symbols or block that represent lexical constructs as if drawing on a canvas. The user is assisted through visual cues and only symbols or blocks can be connected that result in syntactically correct constructs. To this end, the external contours of symbols or blocks representing syntactic elements  1010  and  1020  are shaped like puzzle pieces  1000  and  1050 , allowing only matching pieces to be joined  1040 . On its own, this approach has limited expressiveness: the many dimensions of a programming language are reduced to a flat canvas and a limited domain of possible interconnections. For this approach to extend to more complex problems, a greater degree of expressiveness is needed. This is attained by also shaping internal boundaries of holes traditionally meant for input parameters to accommodate other syntactical elements. This leans on the concept of code not just computing data, but other code. This is higher order logic and a key aspect of functional programming.  FIG. 11  is a block diagram describing higher order visual programming with detail hiding  1100 . Syntax element  1110  has a hole or recess to accommodate parameters  1120 , the design of which is refined through shaping the internal contours in a puzzle-piece like way  1140 . The syntax element  1110  may now accommodate both data types according to shape  1150  as well as other syntax elements according to shape  1160 . This creates the higher order semantics of code accommodating other code and it also gives rise to nested composition of lexical constructs. To make nested composition on a two-dimensional medium feasible, code folding and variable symbol sizing  1130  are employed. This optionally allows detail to be hidden or revealed by unfolding syntax elements or by zooming into relevant level of detail. 
         [0122]      FIG. 12  is a flow diagram describing artificial intelligence assisted visual live programming  1200 . The description in paragraph [0113] centers on the geometric aspects of visual programming. After a user visually edits the program  1210 , the syntax is verified geometrically. Incompatible constructs are prevented from being connected  1220 . Where incompatible constructs are attempted, an error notification is given  1230  and changes are reverted  1240 . If the user wishes to continue  1250 , the edit cycle continues. Where geometric syntax checks were successful, incremental compilation is attempted  1260 . For the technology described in this disclosure, this can be effected simply by connecting the visual programming environment to a Read-Evaluation-Print-Loop (“REPL”). As the user enters new syntactic constructs, they are sent to the REPL for compilation and evaluation. As it is possible to have syntactic constructs that require multiple sub-constructs, it is also possible for an ultimately correct construct to be temporarily incorrect. This happens where a user has completed some, but not all sub-constructs that comprise a higher level construct. For this reason, it is possible to override the results of the incremental compilation validation step. Up to this point, all checks are syntactic only. Step  1280  introduces semantic validation. This is the point where visual product modeling becomes artificial intelligence assisted. As explained, the technology described in this disclosure involves a Knowledge Representation and Reasoning System coupled to a manufacturing ontology and a robotic capability ontology. Constructs submitted in Knowledge Interchange Format can be checked for feasibly given the semantics of the relevant ontology which is to say it is possible, for example, to validate if a given product specification may be built given one or more specific manufacturing capabilities. Step  1280  leverages this ability. Crucially, as explained in section [0059] of this disclosure, the Knowledge Representation and Reasoning System possesses the ability to explain its decisions. This means the user can be guided in product design. As with incremental compilation, it is possible for an ultimately correct construct to be temporarily incorrect while a user completes a series of steps. For this reason, it is possible to override the results of the semantic validation step. Finally, step  1295  renders models as they are created. This gives the system an aspect of what persons of ordinary skill in the art refer to as live coding. Such rendering may be visual or textual. An example of textual rendering might be real-time generation of a manufacturing plan for a product as the product specification is edited.