bluuebunny/crossref_metadata_2025 huggingface dataset:
An agent-based approach to concurrent cable harness design
Authors: Park Hisup , Cutkosky Mark R. , Conru Andrew B. , Lee Soo-Hong
Published: March 2010

An approach to providing computational support for concurrent design is discussed in the context of an industrial cable harness design problem. Key issues include the development of an architecture that supports collaboration among specialists, the development of hierarchical representations that capture different characteristics of the design, and the decomposition of tasks to achieve a trade-off between efficiency and robustness. An architecture is presented in which the main design tasks are supported by agents – asynchronous and semiautonomous modules that automate routine design tasks and provide specialized interfaces for working on particular aspects of the design. The agent communication and coordination mechanisms permit members of an engineering team to work concurrently, at different levels of detail and on different versions of the design. The design is represented hierarchically, with detailed models maintained by the participating agents. Abstractions of the detailed models, called “agent model images,” are shared with other agents. In conjunction with the architecture and design representations, issues pertaining to the exchange of information among different views of the design, management of dependencies and constraints, and propagation of design changes are discussed.
A knowledge-based user interface for the interactive design of three-dimensional objects
Authors: Chan King , Hoeltzel David A.
Published: March 2010

A critical problem with existing computer-based geometric modeling is the labor intensive task involved in specifying data input for the description of three-dimensional (3-D) objects. This paper describes a new, 3-D input system aimed at alleviating this problem. It is based on the use of a three-dimensional digitizer for the direct input of spatial coordinates, and an intelligent interactive user interface. The intent of this system is to create a high level, intelligent interface between the designer and a geometric solid modeler which would lighten the designer's burden in performing arduous 3-D geometric description tasks. The user interface is developed around the Knowledge Craft expert system building tool, using the rule-based Carnegie Representation Language OPS5 (CRL-OPS). The system uses schematic networks or frames and production rules to encode knowledge about geometric primitive digitization methods, object feature operators, solid modeler requirements, and input command functions. It also employs a forward-chaining inference strategy to direct the knowledge. This ensures that only a minimal amount of valid data entry is required by the user. However, if excessive data is entered the intelligent interface has the capability to extract the required information. As a result, the solid modeler can automatically create the appropriate object “primitive” or the specific object “feature” upon recognition by the expert system. It will be demonstrated that these capabilities can simplify the 3-D model description process.
A model of the mechanical design process based on empirical data
Authors: Ullman David G. , Dietterich Thomas G. , Stauffer Larry A.
Published: March 2010

This paper describes the task/episode accumulation model (TEA model) of non-routine mechanical design, which was developed after detailed analysis of the audio and video protocols of five mechanical designers. The model is able to explain the behavior of designers at a much finer level of detail than previous models. The key features of the model are (a) the design is constructed by incrementally refining and patching an initial conceptual design, (b) design alternatives are not considered outside the boundaries of design episodes (which are short stretches of problem solving aimed at specific goals), (c) the design process is controlled locally, primarily at the level of individual episodes. Among the implications of the model are the following: (a) CAD tools should be extended to represent the state of the design at more abstract levels, (b) CAD tools should help the designer manage constraints, and (c) CAD tools should be designed to give cognitive support to the designer.
MAPCon: a case study in a configuration expert system
Authors: Parunak H. V. D. , Kindrick J. D. , Muralidhar K. H.
Published: March 2010

MAPCon II is the second generation (Muralidhar and Irish, IEEE Journal on Selected Areas in Commumcctions 6(5), 869–873, 1988) of an expert system that interactively guides a user in performing off-line configuration for local area networks that use MAP, the manufacturing automation protocol. This paper describes the configuration task in general and MAPCon in particular.

Though MAPCon's purpose is off-line configuration, its problem domain requires that it accomplish other reasoning objectives in addition to those commonly associated with configuration. It is in the process of being expanded into an on-line network supervisor. We develop a taxonomy of reasoning objectives and show how MAPCon combines two different kinds of reasoning to accomplish its objectives. Our experience confirms that of other researchers, and suggests that building robust, practical systems will require us to understand more clearly the interfaces among different reasoning objectives.

The paper has four parts: 1. a definition of configuration and other reasoning objectives; 2. a summary of the problem domain in which MAPCon operates; 3. a description of MAPCon as the user sees it; 4. a look ‘under the hood’ to see how MAPCon combines different objectives.
Encore: an object-oriented knowledge-based system for transformer design
Authors: Garrett J. H. , Jain A.
Published: March 2010

The design of electronic power transformers is an activity that requires the application of well-established algorithms from electromagnetic theory and heuristic, judgmental techniques derived from experience in the design and manufacturing of these devices. This paper describes an object-oriented knowledge-based system, Encore, that was developed at Houston Downhole Sensors (a division of Schlumberger Well Services). Encore combines object-oriented, rule-based, and procedural programming techniques to design 60 Hz power transformers. The system uses a heuristic search strategy to generate design alternatives, and then selects the “best” design based on size and efficiency considerations The heuristics are represented and applied as rules; the electromagnetic components are modelled as frame-like objects. The object-oriented nature of this system facilitated enhancements; by specializing some of the objects comprising a power transformer, a power inductor model was quickly developed. Encore reduces design time from a couple of days to less than an hour; it is being used to design the transformers and inductors of power supplies in new Schlumberger well-logging tools. The system was implemented on a Xerox interlisp Workstation using an object-oriented environment called STROBE.
OARPLAN: Generating project plans by reasoning about objects, actions and resources
Authors: Darwiche Adnan , Levitt Raymond E. , Hayes-Roth Barbara
Published: March 2010

This paper describes OARPLAN, a prototype planning system that generates construction project plans from a description of the objects that comprise the completed facility. OARPLAN is based upon the notion that activities in a project plan can be viewed as intersections of their constituents: objects, actions and resources. Planning knowledge in OARPLAN is represented as constraints based on activity constituents and their interrelationships; the planner functions as a constraint satisfaction engine that attempts to satisfy these constraints. The goal of the OARPLAN project is to develop a planning shell for construction projects that (i) provides a natural and powerful constraint language for expressing knowledge about construction planning, and (ii) generates a facility construction plan by satisfying constraints expressed in this language.

To generate its construction plan, OARPLAN must be supplied with extensive knowledge about construction objects, actions and resources, and about spatial, topological, temporal and other relations that may exist between them. We suggest that much of the knowledge required to plan the construction of a given facility can be drawn directly from a three-dimensional CAD model of the facility, and from a variety of databases currently used in design and project management software. In the prototype OARPLAN system, facility data must be input directly as frames. However, we are collaborating with database researchers to develop intelligent interfaces to such sources of planning data, so that OARPLAN will eventually be able to send high level queries to an intelligent database access system without regard for the particular CAD system in which the project was designed.

We begin by explaining why classical AI planners and domain specific expert system approaches are both inadequate for the task of generating construction project plans. We describe the activity representation developed in OARPLAN and demonstrate its use in producing a plan of about 50 activities for a steel-frame building, based on spatial and topological constraints that express structural support, weather protection and safety concerns in construction planning. We conclude with a discussion of the research issues raised by our experiments with OARPLAN to date.
A fuzzy logic system for expert systems
Authors: Chang Te-Chuan , Ibbs C. William , Crandall Keith C.
Published: March 2010

Using the theory of fuzzy sets, this paper develops a fuzzy logic reasoning system as an augmentation to a rule-based expert system to deal with fuzzy information. First, fuzzy set theorems and fuzzy logic principles are briefly reviewed and organized to form a basis for the proposed fuzzy logic system. These theorems and principles are then extended for reasoning based on knowledge base with fuzzy production rules. When an expert system is augmented with the fuzzy logic system, the inference capability of the expert system is greatly expanded; and the establishment of a rule-based knowledge base becomes much easier and more economical. Interpretations of the system’s power and possible future research directions conclude the paper.
Evolutionary learning of novel grammars for design improvement
Authors: Gero John S. , Louis Sushil J. , Kundu Sourav
Published: March 2010

This paper focuses on that form of learning that relates to exploration, rather than generalization. It uses the notion of exploration as the modification of state spaces within which search and decision making occur. It demonstrates that the genetic algorithm formalism provides a computational construct to carry out this learning. The process is exemplified using a shape grammar for a beam section. A new shape grammar is learned that produces a new state space for the problem. This new state space has improved characteristics.
Learning design concepts using machine learning techniques
Authors: Maher Mary Lou , Li Heng
Published: March 2010

The use of machine learning techniques requires the formulation of a learning problem in a particular domain. The application of machine learning techniques in a design domain requires the consideration of the representation of the learned design knowledge, that is, a target representation, as well as the content and form of the training data, or design examples. This paper examines the use of a target representation of design concepts and the application, adaptation, or generation of machine learning techniques to generate design concepts from design examples. The examples are taken from the domain of bridge design. The primary machine learning paradigm considered is concept formation.
A neural network-based machine learning approach for supporting synthesis
Authors: Ivezic Nenad , Garrett James H.
Published: March 2010

The goal of machine learning for artifact synthesis is the acquisition of the relationships among form, function, and behavior properties that can be used to determine more directly form attributes that satisfy design requirements. The proposed approach to synthesis knowledge acquisition and use (SKAU) described in this paper, called NETSYN, creates a function to estimate the probability of each possible value of each design property being used in a given design context. NETSYN uses a connectionist learning approach to acquire and represent this probability estimation function and exhibits good performance when tested on an artificial design problem. This paper presents the NETSYN approach for SKAU, a preliminary test of its capability, and a discussion of issues that need to be addressed in future work.
A dynamic memory of software designs
Authors: Henderson Scott , Bailin Sidney C.
Published: March 2010

This paper describes an application of artificial intelligence to support software reuse. We begin by discussing the characteristics of software engineering that establish dynamic reorganization as a requirement for a repository of software artifacts. We then present an experimental system that uses incremental concept formation as the basis for dynamic reorganization, and the conceptual hierarchy that was generated by the system for a set of 67 artifacts. The hierarchy is compared to a hierarchy produced manually by independent investigators, and the automatic hierarchy is evaluated in terms of retrieval efficiency and retrieval reliability. The paper ends with a discussion of three projects that share similar objectives with our work.
Introduction: Issues in case-based design systems
Authors: Pu Pearl
Published: March 2010

Serving as a guest editor of this special journal issue, I review in this introductory paper some of the important issues concerning applying case-based reasoning (CBR) techniques to the design domain. Through discussions of these issues, I attempt to give the current status of the case-based design field, with surveys of existing case-based design systems and a summary from the first workshop held on this subject. For those who are less familiar with CBR, this paper also contains a section on what CBR method is and how it is contrasted with other reasoning approaches. The last section of this article introduces the papers included in this issue.
PANDA: A case-based system to aid novice designers
Authors: Roderman Stacy , Tsatsoulis Costas
Published: March 2010

PANDA, the Pumper Apparatus Novice Design Assistant, is a case-based design system developed to assist firefighters who wish to design their pumper engines. In contrast to other such systems, PANDA addresses the unique needs of a novice, non-specialist who performs design in a highly specialized domain, where the design is decomposable into elements which each fulfill their own identifiable function. In PANDA we study how to create an interactive case-based design assistant that can understand the needs and desires of a non-specialist designer and can translate them into formal specifications; that can provide assistance by using case-based design methodologies; that can deal with non-functional design criteria such as aesthetics and traditional practices; and that can guide the novice designer by discussing alternatives, tradeoffs, and adaptations. Our prototypical system verifies our methodological approach to supporting novice design activities.
Activity aggregation in model-based AI planning systems
Authors: Winstanley Graham , Hoshi Kunito
Published: March 2010

When model-based planning systems are scaled up to deal with full-sized industrial projects, the resulting complexity in the project-specific model and production plan can create serious problems, not only in dealing with such complexity computationally, but also in user-acceptance. In the model-based planning system described in this paper, activities are dynamically generated, inherently at the detailed level of individual physical components. However, it is possible to intelligently group together collections of components which would be common to realistic work packages, and hence schedule on the basis of virtual components existing within an abstraction hierarchy. This paper describes a technique of project planning within an integrated design/planning system, which exploits fundamental knowledge of engineered systems and provides powerful and flexible planning functionality.
On the evolution of CAE research
Authors: Dym Clive L. , Levitt Raymond E.
Published: March 2010

Less than a decade ago it seemed that a new paradigm of engineering–called computer-aided engineering (CAE) – was emerging. This emergence was driven in part by the success of computer support for the tasks of engineering analysis and in part by a new understanding of how computational ideas largely rooted in artificial intelligence (AI) could perhaps improve the practice of engineering, especially in the area of design synthesis. However, while this “revolution” has failed to take root or flourish as a separate discipline, it has spawned research that is very different from traditional engineering research. To the extent that such CAE research is different in style and paradigm, it must also be evaluated according to different metrics. Some of the metrics that can be used are suggested, and some of the evaluation issues that remain as open questions are pointed out.
A multifaceted approach to evaluating expert systems
Authors: Adelman Leonard , Gualtieri James , Riedel Sharon L.
Published: March 2010

A multifaceted approach to evaluating expert systems is overviewed. This approach has three facets: a technical facet, for “looking inside the black box”; an empirical facet, for assessing the system’s impact on performance; and a subjective facet, for obtaining users’ judgments about the system. Such an approach is required to test the system against the different types of criteria of interest to sponsors and users and is consistent with evolving lifecycle paradigms. Moreover, such an approach leads to the application of different evaluation methods to answer different types of evaluation questions. Different evaluation methods for each facet are overviewed.
Proof planning: A methodology for developing AI systems incorporating design issues
Authors: Lowe Helen
Published: March 2010

In cases where a domain theory can be successfully expressed in a logical formalism and can be used to formulate a task in that domain in mathematical terms, the task of building sound knowledge-based systems is greatly facilitated. However, it is not immediately obvious how the design aspects of such tasks, where these are an important feature of problem solving, can be incorporated in this approach. Design issues differ from search problems in that there may be several choices, each valid in some sense, but not (necessarily) equally good or equally appropriate in the current context. A case study is described in which a methodology is used based on the development of proof plans. The ability to conduct research according to the Popperian framework of hypothesis, validation, testing, and modification in response to empirical evidence – the hypothetico-deductive approach – seems essential to any rigorous scientific endeavor. It is believed that proof planning is a method which readily exploits this inherently incrementalist approach and could prove to be a powerful tool in designing AI systems.
Improving design and documentation by using partially automated synthesis
Authors: Garcia A. Cristina Bicharra , Howard H. Craig , Stefik Mark J.
Published: March 2010

One of the products of engineering, besides constructed artifacts, is design documentation. To understand how design participants use documentation, designers and typical documentation users were interviewed and protocols were taken of them both creating and using design documentation. The protocols were taken from realistic projects of preliminary design for heating, ventilation, and air conditioning systems (HVAC). The studies of document creation and use revealed three important issues: (1) design participants not only look up design facts; they frequently access documents to obtain information about the rationale for design decisions; (2) the design rationale that they see often is missing from the documents; and (3) design requirements change frequently over a project life cycle so that design documents are often inconsistent and out of date. Recognizing these documentation issues in design practice, a new approach was developed in which documents are no longer static records, but rather interactive design models supporting a case. The feasibility of the approach was demonstrated by constructing a running system and testing it with designers on realistic problems. The costs and benefits of creating and using documentation of design rationale also were analyzed. In particular, the active documents approach was evaluated for a routine, preliminary design in domains where community practice is widely shared and largely standardized. The approach depends on the feasibility of creating a parametric design model for the design domain.
Annotated bibliography on research methodologies
Authors: Reich Yoram
Published: March 2010

This annotated bibliography includes a small sample of sources on various aspects of research methodology from diverse disciplines that influence research on artificial intelligence techniques in engineering design analysis and manufacturing (AIEDAM). Some of these sources are extended edited volumes containing many relevant contributions and pointing to additional references. These volumes are marked by a preceding bullet (•). The bibliography is not comprehensive; it covers only several important subjects, and in each subject it lists several representative contributions ordered chronologically.
ADES: An expert system for ATP design
Authors: Cremonini Roberto , Lamma Evelina , Mello Paola
Published: March 2010

This paper presents an expert system (called ADES, i.e. ATP Design Expert System) for the automatic design of Automatic Train Protection systems (ATP). An ATP system is a railway signalling system constituted by a set of logic circuits that control the safe movement of trains within a railway station.

AI techniques proved feasible to address the particular design problem discussed: ADES is able to rapidly design good control circuits to meet operational requirements by using a well-structured, explicitly represented, in depth knowledge of Automatic Train Protection. The use of AI techniques facilitates the maintenance and extension of ADES to face new or unplanned requirements.

Implementing both the expert system and its environment tools in the PROLOG language, by using meta-interpretation techniques, has led to the rapid prototyping of the overall system. Optimization techniques have also been developed to allow ADES to be efficiently executed.
Using manufacturing process representations
Authors: Becker Lee A. , Bartlett Ron , Kinigadner Arno , Roy Mark
Published: March 2010

An MPR is a deep model of a manufacturing process. It is claimed to be easier to acquire than experiential diagnostic rules, and the acquisition of an MPR can be done either by a knowledge engineer or an intelligent interrogator program. An MPR can be used for simulating processes, for centralized or distributed model-based diagnosis of problems with processes, for designing the processes themselves, for determining the need for quality control testing and sensor checks, for determining when knowledge about the process is incomplete and additional knowledge needs to be acquired, and for compiling diagnostic rules.
Rule-based generation of exploded-views and assembly sequences
Authors: Kroll Ehud , Lenz Ehud , Wolberg John R.
Published: March 2010

Direct planning of assembly sequences is either very complicated computationally, or requires an ‘expert’ user. This paper presents a new approach to the automatic generation of assembly plans which involves two phases. An ‘exploded’ layout of the assembled product is first found from its topological and geometric description by a graph-based procedure. Then, a knowledge-based technique is used for planning of selected assembly sequences by utilizing theoretical as well as heuristic knowledge of mechanical components and assembly processes. Besides serving the assembly planning phase, automatic generation of exploded-views could also be a most desirable feature of any computer-aided design system. An example demonstrates the application of the method to an actual product.
The potential of machine learning techniques for expert systems
Authors: Reich Yoram , Fenves Steven J.
Published: March 2010

Expert systems employing current methodologies suffer from two major problems: they are brittle and their development is time-consuming and tedious.Learning, the key to intelligent human behavior and expertise, has the potential of alleviating these difficulties. The paper reviews a number of machine learning techniques and provides a framework for their classification. The description of each technique is followed by an example taken from the domain of structural design. The applicability of machine learning techniques to expert systems is discussed, including some prototype applications and their shortcomings. Three promising research directions are outlined as a partial solution for the shortcomings.
Symbolic computing in engineering design
Authors: Cline Terry , Abelson Harold , Harris Warren
Published: March 2010

Computer programs that combine traditional numeric methods with symbolic algebra and with specific knowledge of application-based techniques can provide new levels of computational support for engineering design. We illustrate this with a computer-based ‘control engineer’s assistant’. Although this program is focussed on control system design, it demonstrates techniques that should be widely applicable across many engineering disciplines. In particular, we show how, with symbolic computing, a computer-aided design system can usefully simulate engineering models early in the design process, before all (or any) system parameters have been specified numerically. Our system employs a flexible, extensible, object-oriented representation for control systems, which admits multiple mathematical models of designs and provides a framework for integrating tools that operate on diverse representations.
An attribute-space representation and algorithm for concurrent engineering
Authors: Darr Timothy P. , Birmingham William P.
Published: March 2010

This paper presents a novel formulation of the configuration-design problem that achieves the benefits of the concurrent engineering (CE) design paradigm. In CE, all design concerns (manufacturability, testability, etc.) are applied to an evolving design throughout the design cycle. CE identifies conflicts early on, avoids costly redesign, and leads to better products. Our formulation is based on a distributed dynamic interval constraint-satisfaction problem (DDICSP) model. Persistentcatalog agentsmap onto DDICSP variables andconstraint agentsmap onto DDICSP constraints. These agents use a set of operations and heuristics to navigate the design space to eliminate sets of designs until a solution is found. Experimental results show that an architecture where each catalog agent resides on a separate computer has performance advantages over nondistributed approaches.
Indexing and retrieval of cases in a case-based design system
Authors: Raphael B. , Kumar B.
Published: March 2010

This paper discusses the issues related to retrieval of cases in a case-based design system. The limitations of the conventional approaches are brought out, and this is followed by a description of a new approach to indexing and retrieval. This approach aims at capturing control knowledge required for retrieval from individual retrieval examples. Since this approach is based on past retrieval examples, we call it RBEX (Retrieval Based on EXamples). The proposed approach is implemented in a prototype system called CADREM. CADREM uses design methods used in solving past design problems to solve new problems. This is closer to the derivational analogy approach rather than the more common transformational analogy. CADREM has been tried in the domain of conceptual structural design of buildings. Sample outputs from CADREM are also included.
Special Issue: Machine learning in design
Authors: Duffy Alex H.B. , Brown David C. , Maher Mary Lou
Published: March 2010

This issue of AIEDAM is based on a workshop on Machine Learning in Design held at the 1994 Conference on Artificial Intelligence in Design, AID'94, (Gero & Sudweeks, 1994); the second of such workshops, with the first being held at AID'92 in 1992 (Gero, 1992). The first workshop also resulted in a special issue of AIEDAM (Maher et al., 1994).
Reconstructive derivational analogy: A machine learning approach to automating redesign
Authors: Britt B.D. , Glagowski T.
Published: March 2010

This paper describes current research toward automating the redesign process. In redesign, a working design is altered to meet new problem specifications. This process is complicated by interactions between different parts of the design, and many researchers have addressed these issues. An overview is given of a large design tool under development, the Circuit Designer's Apprentice. This tool integrates various techniques for reengineering existing circuits so that they meet new circuit requirements. The primary focus of the paper is one particular technique being used to reengineer circuits when they cannot be transformed to meet the new problem requirements. In these cases, a design plan is automatically generated for the circuit, and then replayed to solve all or part of the new problem. This technique is based upon the derivational analogy approach to design reuse. Derivational Analogy is a machine learning algorithm in which a design plan is saved at the time of design so that it can be replayed on a new design problem. Because design plans were not saved for the circuits available to the Circuit Designer's Apprentice, an algorithm was developed that automatically reconstructs a design plan for any circuit. This algorithm, Reconstructive Derivational Analogy, is described in detail, including a quantitative analysis of the implementation of this algorithm.
From design experiences to generic mechanisms: Model-based learning in analogical design
Authors: Bhatta Sambasiva R. , Goel Ashok K.
Published: March 2010

Analogical reasoning plays an important role in design. In particular, cross-domain analogies appear to be important in innovative and creative design. However, making cross-domain analogies is hard and often requires abstractions common to the source and target domains. Recent work in case-based design suggests that generic mechanisms are one type of abstractions useful in adapting past designs. However, one important yet unexplored issue is where these generic mechanisms come from. We hypothesize that they are acquired incrementally from design experiences in familiar domains by abstraction over patterns of regularity. Three important issues in abstraction from experiences are what to abstract from an experience, how far to abstract, and what methods to use. In this short paper, we describe how structure-behavior-function models of designs in a familiar domain provide the content, and together with the problem-solving context in which learning occurs, also provide the constraints for learning generic mechanisms from design experiences. In particular, we describe the model-based learning method with a scenario of learning feedback mechanism.
Knowledge compilation in routine design problem-solving systems
Authors: Brown David C.
Published: March 2010

We are interested in investigating the different types of knowledge and mechanisms that cause design problem-solving to become routine. We use the model of Routine Parametric Design incorporated in the language DSPL (Brown & Chandrasekaran, 1989), where both the parameters to be given values and the design knowledge needed are known in advance.
Learning to cope with an open world
Authors: Faltings Boi
Published: March 2010

Science has developed detailed and well-founded theories for analyzing the behavior of artifacts. For example, Boeing was able to correctly verify an entirely new airplane, the Boeing 777, before any prototype was even built. However, there are few theories, and no computer systems, that would allow us to design structures with a similar degree of automation.
Improving design problem formulations using machine learning
Authors: Gero John S.
Published: March 2010

Most of machine learning in design has focussed on learning generalizations to predict some future behavior of the system under consideration. Such approaches have been applied primarily to the analysis and synthesis stages of designing. There has been little work done relating to the formulation stage. This paper applies a particular machine learning approach to the improvement of the formal description of the design formulation. It applies an evolutionary technique to the problem reformulation to improve the formulation. This results in both a near optimal problem formulation and an improvement in the solution synthesized from that formulation.
Design agents that learn
Authors: Grecu Dan L. , Brown David C.
Published: March 2010

This research originates in the work started several years ago at Worcester Polytechnic Institute dedicated to the investigation, modelling and evaluation of multiagent based design. The main thrust behind our approach is the idea of finding elementary patterns of agent problem-solving and interaction in design tasks. To achieve this goal we introduced and defined the concept of Single Function Agents, (SiFAs) (Dunskus, 1995; SiFA, 1995). SiFAs are agents specialized to perform one single generic function during the design process. Some typical functions would be selection, evaluation, and critique. These types of agents can be instantiated for different, particular design domains.
Learning about novel operating environments: Designing by adaptive modelling
Authors: Prabhakar S. , Goel A. K.
Published: March 2010

According to Simon (1983), machine learning is a process that improves the performance of an intelligent system. The performance task of a design system is to arrive at a structural description of a device given its functional description. Machine learning can occur, to improve the performance of the design system, in a number of ways. The learning can make the process of arriving at a known structure to function mapping faster, either by improving the control strategy or by learning new world knowledge needed for the structure to function mapping. It can also improve the performance of the design system by allowing it to come up with new structure to function mappings. The latter kinds of learning leads to innovative designs. The work presented in this paper, EnviroAdapt, belongs to the latter category. EnviroAdapt has the performance task of designing devices along with their operating environments. The EnviroAdapt learns about the novel environments and designs devices for these environments. This learning process is enabled by adapting the previous designs of devices along with their operating environments. This adaptation process makes use of abstracting general mechanisms from the previous designs, then instantiating them in the context of new design problem. Both of these processes are driven by the characteristics of the new design problem that requires a device to operate within a novel operating environment.
S-SALT: A problem-solver, knowledge acquisition tool and associated knowledge base refinement mechanisms
Authors: Leo Pietro , Sleeman Derek , Tsinakos Augoustos
Published: March 2010

SALT is the highly influential system implemented by Marcus (1988), which is able to acquire knowledge and solve problems using the propose-and-revise strategy. The primary task to which this has been applied is the design of elevators, or lifts (Marcus et al., 1988). Recently we have re-implemented and extended the system at Aberdeen, S-SALT, the new system, has the following enhancements:
Evolving a design focus in response to design solutions
Authors: Maher Mary Lou
Published: March 2010

Most computer-based design tools assume designers work with a well-defined problem. The traditional treatment of design as two discrete phases; problem formulation and solution synthesis, is challenged by recent research. Though the view on discrete phases may be applicable to simple and/or well-defined design tasks, current research (Jonas, 1993; Logan & Smithers, 1993; Gero, 1994; Smithers et al., 1994) has shown that design is an ill-structured problem and the discrete phases view is not a good description of the process during which design alternatives are generated. A potential role of machine learning techniques is to provide a computational model of the changing representation of the design problem in response to the search for design solutions.
The role of Learning in systems of reusable heterogeneous design agents
Authors: Nagendra Prasad M. V. , Lander Susan E. , Lesser Victor R.
Published: March 2010

In this abstract, we discuss the use of learning techniques to improve performance and solution quality in multiagent parametric design. We have implemented the L-TEAM testbed for empirical evaluation of two forms of learning (described in detail below):
Learning abstract models for system design
Authors: Reddy Sudhakar Y.
Published: March 2010

Though simulation models are extensively used for detailed design analysis, they find limited role in preliminary design decisions. We have developed a machine learning based approach to enable detailed simulation models to be harvested for supporting early-stage design of engineering systems.
Learning representations for creative design using evolution
Authors: Schnier Thorsten , Gero John S.
Published: March 2010

Creative design has been characterized in computational terms as “that design activity which occurs when a new variable is introduced into the design” (Gero, 1994). This is opposed to “routine design,” where “knowledge about variables, objectives expressed in terms of those variables, constraints expressed in terms of those variables, and the processes needed to find values for those variables, are all known a priori.” A third alternative, “innovative design,” occurs when no new variables are introduced, but when one or more variables are used with values outside the usual scope. In computational terms, routine design can also be seen as “search,” (sometimes also called “exploitation”) in so far as a certain, predefined search space is searched for a design solution. For creative design, where the search proceeds outside the boundaries of a predefined search space, the term “exploration” can be used.
Inductive learning for engineering design optimization
Authors: Schwabacher Mark , Ellman Thomas , Hirsh Haym
Published: March 2010

We are working on using machine learning to make the numerical optimization of complex engineering designs faster and more reliable. We envision a system that learns from previous design sessions knowledge that enables it to assist the engineer in setting up and carrying out a new design optimization. We have performed initial experiments for two aspects of setting up an optimization: selecting a prototype to serve as a starting point for the optimization and selecting a reformulation of the search space. Both choices can dramatically affect the speed and the reliability of design optimization.
Computational model for conceptual design based on extended function logic
Authors: Sturges Robert H. , O'Shaughnessy Kathleen , Kilani Mohammed I.
Published: March 2010

Function logic methods have been successfully used in Value Analysis (VA) and Value Engineering (VE) for several decades. This functional approach attempts to provide a common language for specialists in multiple domains. This paper describes an extension of function logic that assists in systematic identification of design functions, allocations, and their interrelations. Our approach identifies a three-level function/allocation/component information structure to represent the state of the design. We illustrate new types of links that exist between functions and the effect of these on the representation of the interrelated functions. These linkages provide new pathways for design information and function evaluation through allocation arithmetic and supported functions. A computational model of the conceptual design process is proposed based on the extended function logic design representation. An outline of the inputs, outputs and operations on form and function variables is given as a step prior to the synthesis process. We illustrate, by example, the process of translating functional representations across specialist domains. Finally, a computer-based aid to developing functional models is described.
Functional descriptions used in computer support for qualitative scheme generation—“Schemebuilder”
Authors: Bracewell R.H. , Sharpe J.E.E.
Published: March 2010

With increased pressures coming from global competition and requirements for greater innovation in product development, designers are hard pressed to deliver designs of higher quality and variety using a repertoire of technological options from different disciplines. This interdisciplinary product development approach has not only removed many of the traditional constraints to design but has now given designers a much wider freedom of choice as to the best solution to a design problem. The focus of this paper is a knowledge-based design environment called Schemebuilder, which is a comprehensive and integrated suite of software tools aimed at supporting the designer in the rapid development of product design models in the conceptual, through embodiment stages of design. Illustrated is the use of the software tools in the qualitative generation of alternative schemes, by application of stored working and decomposition principles in the development of a function-means tree-like information structure. With mechatronic product development as the main theme, this paper describes a closely integrated methodology that incorporates a bond graph approach to continuous-time energetic systems and high-level Petri nets for the rigorous description of discrete-time information systems. Additionally, a technique is suggested for the decomposition of free format statements of need into the rigorously defined design context and required functions, which form the starting point of the function-means development process.
Feature modeling based on design catalogues for principle conceptual design
Authors: Peien Feng , Guorong Xu , Mingjun Zhang
Published: March 2010

The research on new types of design knowledge bases and conceptual solution catalogues is an advanced subject of design methodology. Using the separate function as an example, this paper establishes design catalogues for principle conceptual design to realize the function, and proposes a general computer-oriented action principle design process. Upon studying the relationship of knowledge in the process, this paper represents functions of action principles in function feature models and provides computer-oriented feature models for action principle design.
Genetic algorithm synthesis of four-bar mechanisms
Authors: Roston Gerald P. , Sturges Robert H.
Published: March 2010

The synthesis of four-bar mechanisms is a well-understood, classical design problem. The original systematic work in this field began in the late 1800s and continues to be an active area of research. Limitations to the classical theory of four-bar synthesis potentially limit its application to certain real-world problems by virtue of the small number of precision points and unspecified order. This paper presents a numerical technique for four-bar mechanism synthesis based on genetic algorithms that removes this limitation by relaxing the accuracy of the precision points.
Simulating the behavior of poorly understood continua using neural networks
Authors: Flood Ian
Published: March 2010

This paper proposes and evaluates an artificial neural network based method of modeling the dynamic behavior of spatially distributed continuous engineering processes. The technique is applicable to situations where the differential equations governing the behavior of a system are nonlinear and poorly understood, such as is the case for frost-heave and thaw-settlement processes in soils. A description is first provided of a means of modeling the unknown component of governing differential equations. A range of levels of understanding of these equations is considered. A method of discretizing the resultant neural models of these equations is then illustrated, and the way in which these can be used to simulate the behavior of a process is described. The performance of the proposed neural network approach is then assessed in a series of experiments simulating the nonlinear thermal behavior of translucent solid materials. The system is proven capable of providing highly accurate simulations of system behavior sustained over many thousands of simulation time steps. The paper concludes with an identification of several ongoing areas of further development and application of the proposed tool.
Adaptive uncertainty management for a class of diagnostic expert systems
Authors: Bogunović Nikola , Mesić Tomislav
Published: March 2010

This paper gives a theoretical foundation and implementation details of data and control structures in a diagnostic expert system with adaptive uncertainty management. The considered problem covers a class of expert systems and the application domain in which the operating personnel actively monitor and control the outcome of an industrial manufacturing process. Based on subjective ranking of the latent fault in the final output product, the operator queries the expert system for a recommended corrective action. Subsequent successful or unsuccessful corrective actions consistently modify confidence measures of all corrective recommendations from the same fault set, according to the embedded analytical model. The described approach is based on the relational data representation and procedural control structures, as opposed to a more traditional declarative, production-based system.
Construction project control using artificial neural networks
Authors: Al-Tabtabai H. , Kartam N. , Flood I. , Alex A.P.
Published: March 2010

Artificial neural networks are finding wide application to a variety of problems in civil engineering. This paper describes how artificial neural networks can be applied in the area of construction project control. A project control system capable of predicting and monitoring project performance (e.g., cost variance and schedule variance) based on observations made from the project environment is described. This project control system has five neural network modules that allow a project manager to automatically generate revised project plans at regular intervals during the progress of the project. These five modules are similar in design and implementation. Therefore, this paper will present the main issues involved in the development of one of these five neural network modules, that is, the module for identifying schedule variance. A description of a graphical user interface integrating the neural network modules developed with project management software, and a discussion on the power and limitations of the overall system conclude the paper.