# Augmented Learning and Data Filtering: Knowledge Management and Discovery

Cyrus F. Nourani, Eunika Mercier-Laurent

► To cite this version:

Cyrus F. Nourani, Eunika Mercier-Laurent. Augmented Learning and Data Filtering: Knowledge Management and Discovery. 6th IFIP International Workshop on Artificial Intelligence for Knowledge Management (AI4KM), Jul 2018, Stockholm, Sweden. pp.91-106, 10.1007/978-3-030-52903-1_8 .
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Augmented Learning and Data Filtering:
Knowledge Management and Discovery

Cyrus F Nourani¹, Eunika Mercier-Laurent ²

¹ AFWMTS & Acdmkrd TU Berlin DAI, Berlin, Germany

[Acdmkrd@gmail.com](mailto:Acdmkrd@gmail.com)

² University of Reims Champagne Ardenne, Reims, France
eunika.mercier-laurent@univ-reims.fr

**Abstract.** Ever since the author's publications on frontiers of decision trees, forecasting, and business ecosystems, over a decade the techniques for enterprise business systems planning and design with predictive models have become an attention focus. Heuristics on predictive analytics are developed with novel applications to decision trees. Augmented world and priority-based decision trees are new applications for machine learning and big data filtering. The areas addressed include designing predictive modeling with strategic decision systems with applications to analytics, enterprise modeling, and cognitive social media business interfaces. The areas explored range from plan goal decision tree satisfiability with competitive business models to predictive analytics models that accomplish goals on 3-tier glimpse to business systems. Example decision support application for AI KM with applications is presented. Augmented learning decisions is how AI enhances the decision-making process with more comprehensive cognitive views to business models and infrastructures.

**Keywords:** Augmented model learning, data analytics, decision trees, predictive analytics, enterprise modeling, cognitive spanning.

# 1. Introduction

The areas presented in this paper range from plan goal decision tree satisfiability with competitive business models to predictive analytics models that accomplish goals on a 3-tier business systems design models. The decision-making process and business analytics are explored with an augmentation of decision processes to accomplish goals modeled. Attention spanning trees are applied to focus on plan
goal models that can be processed on a vector state machine coupled with a database pre-processor data mining interfaces (Nourani 200? Pedersen 200?). Modeling, objectives, and planning issues are examined to present precise decision strategies. Competitive decision tree models are applied to agent learning. Enterprise systems stage sequence communications with business objects and basic content management with multi-tier interfaces are being explored, focusing on attention spanning (Nourani-Lauth-Pedersen 2013) with specific examples developed in (Lauth-Nourani-Pederson-Bloom 2013), (Lauth 2013). The field of automated learning and discovery has obvious financial and organizational memory applications. There are basic applications to data discovery and model discovery. Augmentation is a process that enhances decision making by providing cognitive decision support that provides AI argumentation that affects behavior and sentiments to make sensible decisions. Hence it does not replace the human decision-making process with an automated machine.

A competitive business modeling technique, based on the first author's planning techniques are stated in brief. Systemic decisions are based on common organizational goals, and as such business planning and resource assignments should strive to satisfy such goals. Heuristics on predictive analytics are examined with brief applications to decision trees. The basic multi-tiered designs are based on the following layers. The presentation layer contains components dealing with user interfaces and user interaction. Example a visual JAVA standalone application. A business logic layer contains components that work together to solve business problems. The data layer is caused by the business logic layer to persist state permanently. More and more enterprises recognize that in the electronically archived databases a there is a potential for knowledge that could be processed up to now only insufficiently example application for competitive models appears in the transactional business models. Alternate models can be designed based on where assets, resources, and responsibility are assigned; how to control and coordination are distributed; and where the plan goals are set. A transactional international business model might comprise a coordinated federation with many assets and resources. The overseas operations are considered a subsidiary to a domestic central corporation. However, decisions and responsibilities are decentralized. Administrative formal management planning and control systems are how headquarters-subdivision controls are managed. Section outlines are as follows: section 2 presents the basics of competitive goals and models. Agent and/or trees are applied as primitives on decision trees to be satisfied by competitive models. Planning with predictive models and goals are presented with stock forecasting examples from the first author's newer decade's publications. The section concludes with the entrepreneurial cognitive augmented decision processes implications. Section 3 briefs on goals, plans, and realizations with databased and knowledge bases. There a function key interface to the database is presented with
applications to model discovery and data mining. Competitive model goal satisfiability with model diagrams is briefed with examples. Section 4 presents the applications to decision trees and practical systems design with splitting agent decisions trees. Cognitive spanning with decision trees and state vector machine computations applications are presented. Section 5 presents spanning applications with multitier business interfaces. Social media applications with Gatesense example from the accompanying author's is briefed. The paper concludes with heuristics for competitive models and goals for decision tree accomplishment from the first author's newer game decision tree bases since (Nourani-Schulte 2014).

## 2 Competitive Models and Goals

The massive data without data filtering techniques is very prohibitive for business analytics. The process is not based on statistical models for massive data. It is AI that can filter based on data patterns. There is domain knowledge built up from years of experience or technology experts who may be well-versed in data, analytics, or AI. An important AI technique is planning that is based on goal satisfaction at business models. Multiagent planning, for example, as (Muller and Pischel 1994, Bazier et al. 1997), in the paper is modeled as a competitive learning problem where the agents compete on game trees as candidates to satisfy goals hence realizing specific models where the plan goals are satisfied. When a specific agent group “wins” to satisfy a goal the group has presented a model to the specific goal, presumably consistent with an intended world model. For example, if there is a goal to put a spacecraft at a specific planet’s orbit, there might be competing agents with alternate micro-plans to accomplish the goal. While the galaxy model is the same, the specific virtual worlds where a plan is carried out to accomplish a real goal at the galaxy via agents are not. Therefore, Plan goal selections and objectives are facilitated with competitive agent learning. The intelligent languages (Nourani 1996, 1998) are ways to encode plans with agents and compare models on goal satisfaction to examine and predict via model diagrams why one plan is better than another, or how it could fail. Games play an important role as a basis for economic theories. Here the import is brought forth onto decision tree planning. An agent AND/OR tree is an AND/OR tree e.g. (Nourani, 1999), with And/or trees from (Nielsen 1967, Genesereth-Nielsen 1987) where the tree branches are intelligent trees. The branches compute a Boolean function via agents. The Boolean function is what might satisfy a goal formula on the tree. An intelligent AND/OR tree is solved iff the corresponding Boolean functions solve the AND/OR trees named by intelligent functions on the trees. Thus node m might be f(a1,a2,a3) & g(b1,b2), where f and g are Boolean functions of three and
two variables, respectively, and ai's and bi are Boolean valued agents satisfying goal formulas for f and g.

## 2.1 Predictive Models

Predictive modeling is an artificial intelligence technique defined since the first author's model-theoretic planning project over a decade before. It is a cumulative nonmonotonic approximation attained with completing model diagrams on what might be true in a model or knowledge base. A predictive diagram for a theory T is a diagram D (M), where M is a model for T, and for any formula q in M, either the function f: q → {0,1} is defined, or there exists a formula p in D(M), such that T ∪ {p} proves q; or that T proves q by minimal prediction. Prediction involves constructing hypotheses, where each hypothesis is a set of atomic literals; such that when some particular theory T is augmented with the hypothesis, it entails the set of goal literals G. The hypotheses must be a subset of a set of ground atomic predictable. The logical theory augmented with the hypothesis must be proved consistent with the model diagram. Prediction is minimal when the hypothesis sets are the minimal such sets. Plan goal selections and objectives are facilitated with competitive agent learning. The intelligent languages (Nourani 1996,1998) are ways to encode plans with agents and compare models on goal satisfaction to examine and predict via model diagrams why one plan is better than another, or how it could fail. Games play an important role as a basis for economic theories. Here the import is brought forth onto decision tree planning. Newer tree computing techniques are applied to present precise strategies and prove theorems on multiplayer games. Game tree degree concerning models is defined and applied to prove soundness and completeness.

## 2.3 How Augmented Model Decisions are permeated

Augmented intelligence combines person and machine intelligence when filtering data for value creation. Augmenting instincts and intuition with AI algorithms render rapid data-driven predictive insights. These insights can help people redesign functions, detect patterns find strategic opportunities, and turn data into action. We have seen some specifics on the preceding sections intended to extend human cognitive abilities, augmented intelligence is different from straight automation.

Augmented world cognitive decision-making involves a creative mix of data, analytics, and artificial intelligence (AI), with a clever person-machine ambient interaction. The consequent is augmented intelligence with the analytical power and speed of AI managing the big data towards agile, smarter decisions and discovering
patterns. The analytics that is deployed at major companies are not yet at the stage
that one can state examples for augmented, that is an abstraction, for example to
bussiness processes to which one can apply machine intelligence to address lower
lever decisions in infrastructure, for example. fall short of their potential. We present
some techniques, for example, cognitive spanning for decision making.

## 2.4 Entreprenurship Behavior and Decisions Example

Entrepreneurship research (Shönberger 2016) focuses on what type of personality
entrepreneurs have. Since the activities are new in certain respects the uncertainty and
unpredictability are central characteristics of entrepreneurship. Research has shown
that certain personality traits correlate to the propensity to engage in entrepreneurship.
(Gartner 1989) presents bases that behavior, rather than personality traits, that permeate
entrepreneurial decision making: to engage or not to engage decisions in certain
entrepreneurial activities. When AI and behavior are the considerations the
paradigm shift in how man and machine will work together is behavior-driven,
therefore, amenable to argumentation. Equally important, you need to have the right
models and processes in place, i.e. the recipes for success.

One would like to have APS that processes data with AI and create predictive
models to make predictive recommendations e.g. (Nourani 2017- TUB EM). But these
models do not exist in a vacuum. They involve inputs and outputs that impact the rest
of your business. You have to think about how these models fit in and how to prioritize
the insights from data. Moreover, you need governance over augmented intelligence to
see that the automation is working and people know their role in the new man-meets-
machine workforce. The innovation ecosystem is based on decision processes that are
augmentation critical. Self-regulated innovations inn self-managing teams are
additionally important areas for the behavior and decision examples (Careml´-Annosi,
Brunetta, Magnusen 2017) and (Mercier-Laurent 2017) are example overviews for the
ecosystem processes on innovations. ( Bullard and Duffy 1998) and (Nourani-Lauth-
Pedersen 206) are the past glimpses to the economies for decision processes considered
here.

## 3 Goals, Plans, and Knowledge Bases

Practical systems are designed by modeling with information, rules, goals, strategies,
and information onto the data and knowledge bases, where masses of data and their
relationships and representations are stored respectively. Example analytics systems
on the agenda are Watson Analytics: a cognitive system that sifts through massive data
to discover insights that can help its users answers to the most complex of questions. It
can reach for relevant answers in the context of questions. Furthermore can become
smarter, learning from each interaction with users, and each piece of data it interacted
with. Watson can “think” or “reason” similar to a real person. It processes information,
draws conclusions, and learns from its experiences.

With our agent augmented decision trees with forward chaining, that is a goal
satisfaction technique where inference rules are activated by data patterns, to
sequentially get to a goal by applying the inference rules, allows decisions on the
surface meta-data on an augmented abstraction with keyed data functions. The current
pertinent rules are available at an ‘agenda’ store. The rules carried out will modify the
database. Backward chaining is an alternative based on an opportunistic response to
changing information. It starts with the goal and looks for available premises that might
be satisfied to have gotten there. Goals are objects for which there is automatic goal
generation of missing data at the goal by recursion backward chaining on the missing
objects as sub-goals. Data unavailability implies a search for new goal discovery.

A basis to model discovery and prediction planning is presented in (Nourani 2002) and is briefed here. The new AI agent computing business bases defined during the last several years can be applied to present precise decision strategies on multiplayer games with only perfect information between agent pairs. The game trees are applied to improve models. The computing model is based on novel competitive learning with agent multiplayer game tree planning. Specific agents are assigned to transform the models to reach goal plans where goals are satisfied based on competitive game tree learning. The planning applications include OR-ERP and EM as goal satisfiability. Minimal prediction is an artificial intelligence technique defined since the author’s model-theoretic planning project. It is a cumulative nonmonotonic approximation attained with completing model diagrams on what might be true in a model or knowledge base.

**3.1 Decision-Theoretic Planning**

A novel basis to decision-theoretic planning with competitive models was presented in (Nourani 2005) and (Nourani-Schulte 2013) with classical and non-classical planning techniques, see for example, (Hedeler et.al. 1990, Wilkins 1984) from artificial intelligence with games and decision trees providing an agent expressive planning model. We use a broad definition of decision-theoretic planning that includes planning techniques that deal with all types of uncertainty and plan evaluation. Planning with predictive model diagrams represented with keyed KR to knowledge bases is presented. Techniques for representing uncertainty, plan generation, plan evaluation, plan improvement, and are accommodated with agents, predictive diagrams, and competitive model learning. Modeling with effector and sensor uncertainty, incomplete knowledge of the current state, and how the world operates is treated with agents and competitive models.
Bounds on game trees were developed based on the first author's preceding publications on game trees generalizations on VMK to on (Nourani-Schulte 2013). Partial deductions in this approach correspond to proof trees that have free Skolemized trees in their representation. Our past decade developments have applied diagrams do for knowledge discovery knowledge management. Diagrams allow us to model theoretically characterize incomplete KR. To key into the incomplete knowledge base. The following figure depicts selector functions Fi from an abstract view grid interfaced via an inference engine to a knowledge base and in turn onto a database.

<figure><img src="image_2.png" /><figcaption>Figure 3 Keyed Data Functions, Inference, and Model Discovery
Adapted from (Nourani 2014) & Cognitive MIT Sloan Reviews</figcaption></figure>

Generalized predictive diagrams are defined, whereby specified diagram functions and the search engine can select onto localized data fields. A Generalized Predictive Diagram, is a predictive diagram where D (M) is defined from a minimal set of functions. The predictive diagram could be minimally represented by a set of functions {f1,...,fn} that inductively define the model. The functions are keyed onto the inference and knowledge base to select via the areas keyed to, designated as Si's in figure 1, and data is retrieved Nourani [20]. Visual object views to active databases might be designed with the above. The trees defined by the notion of provability implied by the definition might consist of some extra Skolem functions {g1,...,gn}, that appear at free trees. The f terms and g terms, tree congruences, and predictive diagrams then characterize deduction with virtual trees Nourani [12] as intelligent predictive interfaces. Data discovery from knowledge diagrams might be viewed as satisfying a goal by getting at relevant data which instantiates a goal. The goal formula states what relevant data is sought.

## 3.2 Competitive Models and Goal Satisfiability

Business intelligence interfaces might apply automated learning and discovery-often called data mining, machine learning, or advanced data analysis has new w-interface relevance. There are obvious financial and organizational memory applications. E-business, trustworthiness, usability, Human-Computer Interaction, cognitive
ergonomics, user interface design, ease of use, interaction design, and online marketing, are the business user modeling issues areas to address. Consider an example ERP system to optimize a business plan with task assignments based on team-play compatibility. Generic model diagrams are basic function-based data modeling techniques the first author put forth over a decade ago to characterize a business domain, with for example (Nourani-Loo 1999), business objects on a minimal function base.

Remark: The functions above are those by which a business model could be characterized by some schemes, e.g. stock forecasting scheme example e.g. (Nourani. 2018). The computing specifics are based on creating models from generic model diagram functions where basic models can be piece-meal designed and diagrams completed starting from incomplete descriptions at times. Models uphold to a deductive closure of the axioms modeled and some rules of inference, depending on the theory. By the definition of a diagram, they are a set of atomic and negated atomic sentences. Thus the diagram might be considered as a basis for a model, provided we can by algebraic extesion, define the truth value of arbitrary formulas instantiated with arbitrary terms.

## 4. Decision Trees and Vector Spanning Cognitive Spaces

Game theory is the study of rational behavior in situations in which choices have a mutual effect on one's business and competitors. The best decision depends on what others do, and what others do may depend on what they think you do. Hence games and decisions are intertwined. A second stage business plan needs to specify how to assign resources concerning the decisions, ERP plans, and apply that to elect supply chain policies, which can in part specify how the business is to operate. The splitting agent decision trees have been developed independently by (Nourani ECAI 1994). The computing model is based on novel competitive learning with agent multiplayer game tree planning. For example, when arranging team playing, there are many permutations on where the players are positioned. Every specific player arrangement is a competitive model. There is a specific arrangement that does best in a specific game. What model is best can be determined with agent player competitive model learning.

### 4.1 Cognitive learning-based decisions

Cognitive agents are software agents included in the higher-level performance of autonomous intelligent systems. They belong to the class of autonomous agents which are complex computing entities active in some kind of environment without the direct intervention of humans or other virtual systems. For an overview on what was our beginning, examples are (Nourani 2005) (Nourani, Lauth, et.al. 2017). To design intelligent agents systems, flexible problem-solving behavior, and adequate knowledge
about the beliefs regarding the environment and its changing conditions is required. We
refer here to humans, as well as to virtual systems and we try to look at some general
characteristics of new cyber-physical systems. Multi-agent planning is modeled as a
competitive learning problem where the agents compete on game trees as candidates to
satisfy goals hence realizing specific models where the plan goals are satisfied.
Example agenda can distinguish and prioritize Commitments: Committed plans
Committed goals Intended goals and Intended plans, (Kinny, Georgeff, and Rao 1996).

## 4.2 Approaches to cognitive systems

Three broad approaches have been adopted so far for implementing cognitive concepts into autonomously acting systems: Data modeling approach that infers the cognitive concepts from the modular data structures to means-end reasoning system through a theorem prover. An example is a cognitive agent system called Artimis[18], an intentional system designed for human interaction and applied in a spoken-dialog interface for human information access. Procedural approaches that use explicit representations of cognitive contents approach based on the BID: belief, intention, desire agent models and can be instantiated in a procedural reasoning system (PRS), like e.g. MARS [18]. Most cognitive systems fall into this second category. The newest approach is the "situated automata" approach that has no explicit representations of the cognitive concepts and therefore seems to perform better than other approaches in settings where higher performance is expected. Cognitive agents are present in complex applications trying to solve efficiently: context-sensitive behavior, adaptive reasoning, ability to monitor and respond to a situation in real-time (immersive agents), and modeling capabilities based on an understanding of human cognitive behavior, like innovation management, generation of new insights, e.g. new ways of thinking. Further application areas are ubiquitous computing so far as person-machine exchanges might be perceived (Cadrina Lauth,, Berendt, B. et.al). Example research on cognitive agents are on the first author`s publications at CBS Copenhagen, e.g. (Nourani 2005) on affective haptic logic, and (Huhn and Sihng 1988).

## 4.3 Splitting Trees and the CART Model

The following examples from (Nourani 2010) can be motivating for business
applications. Example: A business manager has 6 multitalented players, designed with
personality codes indicated with codes on the following balls. The plan is to accomplish
5 tasks with persons with matching personality codes to the task, constituting a team.
Team competitive models can be generated by comparing teams on specific
assignments based on the task area strength. The optimality principles outlined on the
first author's publications might be to accomplish the goal with as few a team grouping
as possible, thereby minimizing costs. The following section presents new agent
game trees the author had put forth [26]. Applying game theory to business is tantamount to interactive decision theory. Decisions are based on the world as given. However, the best decision depends on what others do, and what others do may depend on what they think you do. Hence games and decisions are intertwined. A second stage business plan needs to specify how to assign resources concerning the decisions, ERP plans, and apply that to elect supply chain policies, which can in part specify how the business is to operate. A tactical planning model that plans critical resources up to sales and delivery is a business planner's dream. Planning and tasking require a definition of their respective policies and processes; and the analyses of supply chain parameters. The above are the key elements of a game, anticipating behavior, and acquiring an advantage. The players on the business planned must know their options, the incentives, and how do the competitors think.

Example premises: Strategic Interactions Strategies :{ Advertise, Do Not Advertise} Payoffs: Companies' Profits Advertising costs Euro million. The And vs. Or principle is carried out on the above trees with the System to design ERP systems and manage as Cause principle decisions. The agent business modeling techniques the author had introduced [25,28] apply the exact 'system as cause' and 'system as symptom' based on models (assumptions, values, etc.) and the 'system vs. symptom' principle via tracking systems behavior with cooperating computational agent trees. The design might apply agents splitting trees, where splitting trees is a well-known decision tree technique. Surrogate agents are applied to splitting trees. The technique is based on the first author's intelligent tree project ECAI 1994 and European AI Communication journal are based on agent splitting tree decisions like what is designed later on the CART system: The ordinary splitting tree decisions are regression-based, developed at Berkeley and Stanford (Brieman, Friedman, et.al. 1984), (Breiman 1996).

CART system deploys a binary recursive partitioning that for our system is applications for the agent and/or trees (Nourani 1999) The term “binary” implies that each group is represented by a “node” in a decision tree, can only be split into two groups. Thus, each node can be split into two child nodes, in which case the original node is called a parent node. The term “recursive” refers to the fact that the binary partitioning process can be applied over and over again. Thus, each parent node can give rise to two child nodes and, in turn, each of these child nodes may themselves be split, forming additional children. The term “partitioning” refers to the fact that the dataset is split into sections or partitioned. CART trees are much simpler to interpret than the multivariate logistic regression model, making it more likely to be practical in a clinical setting. Secondly, the inherent “logic” in the tree is easily apparent.

The agent splitting decision trees have been developed independently since (Nourani 1994-ECAI). For new directions in forecasting and business planning (Nourani 2002). Team coding example diagram from reach plan optimal games where a project is managed with a competitive optimality principle is to achieve the goals minimizing costs with the specific player code rule first author and company 2005.
More recent areas are optimized decisions based on goal reachability (CFNourani-Cadrina Lauth 2018).

## 4.4 Designing an Augmented Learning Model Decision Support Systems

The formal compositional framework for modeling multi-agent tasks DESIRE is introduced here. The following aspects are modeled and specified: (1) a task (de)composition, (2) information exchange, (3) sequencing of (sub)tasks, (4) subtask delegation, (5) knowledge structures. Information required/produced by a (sub) task is defined by input and output signatures of a component. The signatures used to name the information are defined in predicate logic with a hierarchically ordered sort structure (order-sorted predicate logic). Units of information are represented by the ground atoms defined in the signature. The role information plays within reasoning is indicated by the level of an atom within a signature: different (meta) levels may be distinguished.

In a two-level situation, the lowest level is termed object-level information, and the second level meta-level information. Some specifics and a mathematical basis to such models with agent signatures might be obtained from [34]. Meta-level information contains information about object-level information and reasoning processes; for example, for which atoms the values are still unknown (epistemic information). Similarly, tasks that include reasoning about other tasks are modeled as meta-level tasks with respect to object-level tasks. Often more than two levels of information and reasoning occur, resulting in meta-meta-information and reasoning. Information exchange between tasks is specified as information links between components. Each information link relates to output of one component to the input of another, by specifying which truth-value of a specific output atom is linked with which truth value of a specific inputs.

# 5. Spanning and the Multitier Models

## 5.1 Spanning Attention on Decision Trees

When a specific agent group "wins" to satisfy a goal, the agent group is presenting a model consistent with an intended world model for that goal. For example, if there is a goal to put a spacecraft at a specific planet's orbit, there might be competing agents with alternate micro-plans to accomplish the goal [38]. While the galaxy model is the same, the specific virtual worlds where a plan is carried out to accomplish a real goal at the galaxy via agents are not. Therefore, plan goal selections and objectives are based on the attention spans with competitive agent learning. This technique can be also used to solve highly interacting communication problems in a complex web application, web intelligence settings. The intelligent languages (Nourani 1996,1997) are ways to encode plans with agents and compare models on goal satisfaction to examine and
predict via model diagrams why one plan is better than another, or how it could fail. The state space agent modeling present techniques to span the attention state space, where specific agents with internal state set I can distinguish their membership. State vector machines-SVM, agent vectors, and data mining referring to the sections above are applied to design cognitive spanning. SVM creates a set of hyper-planes in N-dimensional space that is used for classification, regression, and spanning in our approach. The SVM algorithms create the largest minimum distance to have competitive goals satisfied on models.

The BID-architectures upon which specifications for compositional multi-agent systems are based are the result of analysis of the tasks performed by individual agents and groups of agents. Task (de)compositions include specifications of interaction between subtasks at each level within a task (de)composition, making it possible to explicitly model tasks that entail interaction between agents. The formal compositional framework for modeling multi-agent tasks DESIRE is introduced here. The following aspects are modeled and specified: (Brazier- Dunin-Keplic., Jennings, Truer 1997)

(1) a task (de)composition, (2) information exchange, (3) sequencing of (sub)tasks, (4) subtask delegation, (5) knowledge structures. Information required/produced by a (sub) task is defined by input and output signatures of a component. The signatures used to name the information are defined in a predicate logic with a hierarchically ordered sort structure (order-sorted predicate logic). Units of information are represented by the ground atoms defined in the signature. The role information plays within reasoning is indicated by the level of an atom within a signature: different (meta) levels may be distinguished. In a two-level situation, the lowest level is termed object-level information, and the second level meta-level information. Some specifics and a mathematical basis to such models with agent signatures might be obtained.

Meta-level information contains information about object-level information and reasoning processes; for example, for which atoms the values are still unknown (epistemic information). Similarly, tasks that include reasoning about other tasks are modeled as meta-level tasks with respect to object-level tasks. Often more than two levels of information and reasoning occur, resulting in meta-meta-information and reasoning. Information exchange between tasks is specified as information links between components. Each information link relates output of one component to input of another, by specifying which truth-value of a specific output atom is linked with which truth value of a specific input atom. For a multi-agent object information exchange model, see, for example (Nourani 1996).
one or more web servers. Business Logic layer Runs with the address space of one
or more application servers, and a Backend and DataBase layer.

The presentation layer contains components dealing with user interfaces and user
interaction. Example a visual JAVA standalone application. A business logic layer
contains components that work together to solve business problems. The components
can be high -performance engines. Data layer is used by the business logic layer to
persist state permanently. Control f the data layer is one or more databases that home
the standalone. From an example content processing prototype (Nourani 2007,2013)
we can glimpse on the applications for the above section. A basic keyed database view
provides the presentation of the model. It is the look The presentation of the model. It
is the look of the application. We apply predefined user know functions on the view to
present the applications look. The view should be notified when changes to the model.

The business logic updates the state of the model and helps control the flow of
the application. With Struts, this is done with an Action class as a thin wrapper to the
actual business logic. The model represents the world model for the actual business
state of the application. The business objects update the application state. application.
The business objects update the Ap. Action Form bean represents the Model state at a
session or request level, bean represents the Model state at a session or request level,
and not at a persistent level. The JSP file reads information from the om the ActionForm
bean using JSP tags. Our design applies the same functions that are presented on the
that are the view for specific application to generate a content model for specific
applications.

6. Augmented Learning and Knowledge Management

6.1 Augmented learning with smart data

Competitive model planning above selects big data segments spanning infinite data.
Possible big data is encoded on model diagrams with nondeterminism minimizing data
segment spans. Big Data has a big value, it also takes organizations big effort to manage
well and an effective governance discipline can fulfill its purpose. The Big Data
Exponentials: Content, Apps: Consumers have been pledging their love for data
visualizations for a while now, and data mining with multimedia discovery is the area
being explored. Big data is a popular term used to describe the exponential growth and
availability of data, both structured and unstructured. More accurate analyses may lead
to more confident decision making. And better decisions can mean greater operational
efficiencies, cost reductions, and reduced risk. Our novel techniques apply non-
deterministic data model diagram filters to span big data spaces. Smart data is big data
turned into actionable data that is available in real-time. Smart Data: What Purpose,
Users,

Processes,

Platform.

Users with data analysis, IT and marketing knowledge, can define processes, enabling processes to take advantage of smart data analytics platforms that can be deployed for the benefits of SmartData: efficiently scalable over many marketing processes. With smart data, we focus on valuable data and often smaller data sets that can be turned into actionable data and effective outcomes to address customer and business challenges. On our decision tree spanning techniques (Nourani and CBS group: Nourani-Ina Lauth-Rassmus Pedersen 2015) a specific cooperating group “wins” to satisfy a goal, the agent group is presenting a model consistent with an intended world model for that goal. The value of big data is only multiplied by good data governance. Our techniques are in part analogical augmented learning applied to model discovery and data management. Eric Klopfer on an MIT press book 2011 describes the largely untapped potential of mobile learning Klopfer argues that the strengths of the mobile platform—its portability, context sensitivity, connectivity, and ubiquity—make it ideal for learning in schools. These games—either participatory (which require interaction with other players) or augmented reality (which augment the real world with virtual information. Our data modeling and discovery techniques apply competitive model diagrams with cooperating learning agents acting on game trees to manage knowledge.

## 6.2 Competitive Model Learning Heuristics

The first author had developed free proof tree techniques since projects at TU Berlin, 1994. Free proof trees allow us to carry on Skolemized tress on game tree computing models, for example, that can have unassigned variables. The techniques allow us to carry on predictive model diagrams realized on plans with free proof trees (Nourani 1994-2007). Thus essentially the basic heuristics here are satisfying nodes on agent AND./OR game trees. The general heuristics to accomplish that is a game tree deductive technique based on computing game tree unfoldings projected onto predictive model diagrams. Newer areas are on a volume chapter (Nourani 2018, editor), for example (Nourani-Lauth 2018) on impact competitive decision tree models. The soundness and completeness of these techniques, e.g, heuristics as a computing logic are published since (Nourani 1994) at several events e.g. AISB 1995, and Systems and Cybernetics 2005), (Nourani 2015). In computer science, specifically in algorithms related to pathfinding, a heuristic function is said to be admissible if it never overestimates the cost of reaching the goal, i.e. the cost it estimates to reach the goal is not higher than the lowest possible cost from the current point in the path. [1] An admissible heuristic is also known as an optimistic heuristic.

An admissible heuristic is used to estimate the cost of reaching the goal state in an informed search algorithm. The heuristic nomenclature indicates that a heuristic function is called an admissible- heuristic if it never overestimates the cost of reaching
the goal, i.e. the cost it estimates to reach the goal is not higher than the lowest possible
cost from the current point in the path. An admissible heuristic is also known as an
optimistic heuristic (Russell and Norvig 2002). We shall use the term optimistic
heuristic from now on to save ambiguity with admissible sets from mathematics, e.g.
first author's publications on descriptive computing, for the time being. What is the
cost estimate on satisfying a goal on an unfolding projection to model diagrams, for
example with SLNDF, to satisfy a goal? Our heuristics are based on satisfying
nondeterministic Skolemized trees. The heuristics aim to decrease the unknown
assignments on the trees. Since at least one path on the tree must have all assignments
defined to T or F, and at most one such assignment closes the search, the "cost
estimate," is no more than the lowest. To become more specific how game tree node
degrees can be ranked, we state one example linear measure proposition since
(Nourani-Schulte 2015). That was further extrapolated for big data heuristics in
(Nourani-Fähndrich 2017). The big Data Sparse Heuristics,e.g (Nourani 2016)
(Nourani-Fähndrich 2017:TU Berlin) agent state vectors are spanning with models for
diagram values that either, true, false, or X- undetermined. The cross product with the
model diagram for vectors is a matrix. That matrix is sparse coding to bigdata with the
X's sparsing the matrix (Nourani 1992, ScandinaviaAI), thus minimizing reaches for
bigdata: hence sparse heuristics are entailed.

7. Conclusions

New bases for augmented model decision techniques with splitting decision tree for
enterprise modeling and business planning with predictive analytics models were
presented. Decision tree applications to analytics towards designing cognitive
augmented business interfaces with applications to social media were presented.
Augmented reasoning on business systems reflect on the innovation ecosystems. Data
filtering is applied with function keyed knowledge bases for goal satisfiability with
competitive business models coupled with predictive analytics models accomplish
goals on business models. Spanning trees focus on plan goal models that can be
processed on a vector state machine coupled with database preprocessor interfaces.
Cognitive views and augmented models with heuristics on predictive analytics are
developed based on ranked game trees from the first authors preceding publications on
economic games and big data towards newer applications to decision tree heuristics.
Newer areas on predictive analytics and impact competitive models are on an edited
volume (Nourani 2018, editor).

References
1. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees. Chapman & Hall (Wadsworth, Inc.): New York, 1984.

2. Cyrus F Nourani, W---Interfaces, Business Intelligence, and Content Processing, Invited Industry Track Keynote, IAT---Intelligent Agent Technology, Atlanta, November 2013.

3. Cyrus Nourani, Intelligent Tree Computing, Decision Trees And Soft OOP Tree Computing September 2, 1997 Frontiers in Soft Computing and Decision Systems Papers from the 1997 Fall Symposium, Boston Technical Report FS-97-04 AAAI ISBN: 1-57735-079-0 www.aaai.org/Press/Reports/Symposia/Fall/fs-97-04.html Programming

4. Cyrus F. Nourani, Codrina Lauth, Rasmus Uslev Pedersen, Agent Planning Models, and Attention Span – A Preliminary, IAT, Atlanta, November 2013.

5. Wooldridge, M. and Jennings, N.R. "Agent theories, architectures, and languages: a survey", In M. Wooldridge and N.R. Jennings, Intelligent Agents - Intelligent Multimedia New Computing Techniques, Design Paradigms, and Applications, 1999. 51-92.

6. Cyrus F. Nourani Model Discovery for Knowledge Management, June 1998. AAAI Wkshp on Agent-Based Systems in the Business Context Orlando, Florida, July 1999. AAAI Press.

7. Caremla'-Annosi, Brunetta, Magnusson 2017) Self-organizing coordination and control approach: The impact of social norms and self-regulated innovations Activities in self-managed teams, in Ecosystems and Technology: Idea Generation and Content Model Processing, Chapter 3, Edited volume: Cyrus Nourani editor, Apple Academic Press, Canada-USA.

8. Nourani, Cyrus F. 2004, Model Discovery, Intelligent W-Interfaces, and Business Intelligence with Multitier Designs" CollecteR LatAm 2004 13 - 15 October 2004, Santiago, Chile http://ing.utalca.cl/collector/techsession.php

9. Nourani, F. Cyrus 1999, Competitive Models and Game Tree Planning, November 1999 Applications to Economic games. A version published at SSGRR, L'Auquila, Rome, Itlay, September 2001. Invited paper.

10. Cyrus F. Nourani, Business Planning and Cross-Organizational Models, Workshop on Collaborative Cross-Organisational Process Design. Linz, Austria, September 2005 http://www.mensch-und-computer.de/mc2005.

11. Schönberger, Helmut, 2016, Systematic spin-off processes in university-industry ecosystem, 93-107, in Academic Spinn-offs and Technology Transfer in Europe: Best Practices and Breakthrough Models, Sven H.D. Cleyn and Günter Festel, editors. Elgar Publishing Ltd. Chellenham UK, and Northampton US, 2016.

12. Nourani, CF. and O. Schulte, 2014. Multiplayer Games, Competitive Models, Descriptive Computing, EUROPMENT conference Computers, Automatic Control, Signal Processing and Systems Science, ISBN: 978-1-61804-233-0.
13. Brazier, F.M.T., Jonker, C.M., "Treur, J., Formalization of a cooperation model based on joint intentions". In Lecture Notes in Computer Science, Volume 1193, 1997, 141-155

14. Cyrus F Nourani*, Johannes Fähndrich+, A Formal Approach to Agent planning with inference trees, NAEC, Trieste, August 2015.

15. Brazier, F. M. T., Dunin-Keplicz, B. M., Jennings, N. R. and Treur, J. "DESIRE: Modeling Multi-Agent Systems in a Compositional Formal Framework". In International Journal of Cooperative Information Systems, 6, (1), 1997, 67-94.

16. Nourani, C.F. 2015 A Predictive Tableaux Visual Analytics, Slovakia, ICTIC - PROCEEDINGS IN CONFERENCE OF INFORMATICS AND MANAGEMENT SCIENCES Volume: 4, Issue: 1, March 2015. Publisher: EDIS - Publishing Institution of the University of Zilina Powered by Thomson, Slovakia. ISSN: 1339-9144, CD ROM ISSN: 1339-231X ISBN: 978-80-554-1002-9

17. 155GATESENSE – Making Sense of Massive Open Data Resources into Sustainable Business Solutions, Codrina Lauth* *** – Lasse Vestergaard**– Rasmus Blom*** - Sead Bajrovic*** – Magdalena Wiktoria Marcynova*** - Rasmus Uslev Pedersen* * Copenhagen Business School, IT Management Dept.

18. Cyrus Nourani, F: and Johannes Fähndrich, Decisions, Inference Trees, and Big Data Heuristics in Computing Predictive Analytics, Business Intelligence, and Economics Modeling Techniques with Start-ups and Incubators, 2018. Buch. 275 S.: 33 s/w-Abbildungen, 14 Farbabbildungen. Hardcover Taylor & Francis ISBN 978-1-1960423-5-1

19. Jen Underwood, IBM Cognitive Analytics overview, 2019,?
20. Bullard, James & Duffy, John, (1998), "A model of learning and emulation with artificial adaptive agents," Journal of Economic Dynamics and Control, Elsevier, vol. 22(2), pages 179-207,2.

21. Cyrus F Nourani and Cadrina Lauth 2018, Strategic Decision Trees on Impact Competitive Models, in Predictive Analytics and Economics, Apple Academic Press.

22. Genesereth, M.R. and Nilsson, N.J. Logical Foundations of Artificial Intelligence, Morgan-Kaufmann, 1987.

23. Nourani, C.F. 2002, Game Trees, Competitive Models, and ERP, New Business Models and Enabling Technologies, Management School, St Petersburg, Russia, Keynote Address, June 2002. Fraunhofer Institute for Open Communication Systems, Germany- Proceedings Editor Nikolai Krivulin.

24. Huhns, M., Singh, M.P., "Cognitive Agents", In IEEE Internet Computing, Nov-Dez. 1998.

25. Kinny, D., Georgeff, M.P., and Rao, A.S. " A Methodology and Technique for Systems of BID Agents", In: W. van der Velde, J.W. Perram (Eds.), Agents Breaking Away, Proc. 7th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, MAAMAW'96, Lecture Notes in AI, vol. 1038, Springer 1996.
26. Lauth, Cadrina., Berendt, B., Pfleging, B. and Schmidt A. "Ubiquitous Computing." In Al. Mehler and Romary, L. (Eds.) Handbook of Technical Communication, Walter de Gruyter, 2012.

27. Cyrus F. Nourani" and Oliver Schulte, Multiagent Decision Trees, Competitive Models, and Goal Satisfiability, DICTAP, Ostrava, Czech Republic, July 2013.

28. Cyrus F. Nourani*, Codrina Lauth**, Rasmus Uslev Pedersen Merging Process-Oriented Ontologies with Cognitive Agent, Planning and Attention Spanning, Open Journal of Business Model Innovations, April 2015.

29. Moore, R.C. 1980, "Reasoning about Knowledge and Action", In AI Center Technical Note 191, SRI International Menlo Park, California, 1980.

30. Nourani, C.F. "Planning and Plausible Reasoning in Artificial Intelligence, Diagrams, Planning, and Reasoning", Proc. Scandinavian Conference on Artificial Intelligence, Denmark, May 1991, IOS Press.

31. Nourani, C.F., and Th. Hoppe 1994, "GF-Diagrams for Models and Free Proof Trees", Proceedings the Berlin Logic Colloquium, Humboldt University, Mathematics Department, May 1994.

32. Nourani, C.F. "Slalom Tree Computing- A Computing Theory for Artificial Intelligence, June 1994, Revised in A.I. Communication Volume 9, Number 4, December 1996, IOS Press,

33. Pedersen, R.U. 2010, "Micro Information Systems and Ubiquitous Knowledge Discovery" In: Lecture Notes in Computer Science, No. 6202, 2010, 216-234.

34. Nourani, C.F., and Grace S.L. Loo, (1998), "KR and model discovery from active DB with Predictive logic," The University of Auckland MSIS, New Zealand International Conference on Business and Financial Applications of Data Mining, Cambridge University, England, August 2000.

35. Nourani, F. Cyrus 2018, Competitive models Augmented Learning and Big Data filtering Heuristics, On IJCCAI Stokholm, 6th AI4KM 2018 : AI-based Innovation in Digital Services, 15 July 2018.

36. Rao, A.S. and Georgeff, M.P. "Modeling rational agents within a BID-architecture", In: R. Fikes and E. Sandewall (eds.), Proceedings of the Second Conference on Knowledge

37. Nourani, C.F. 1998, Business Modeling and Forecasting, November 1998. AIEC-AAA199, Orlando, July 1999.AAAI Press.

38. Nourani, C.F. 2005 Intelligent Multimedia Computer Science- Business Interfaces, Wireless Computing, Databases and Data Mining, American Scientific and publishers, http://www.aspbs.com/multimedia.htmlDecember2004.

39. Nourani, C.F. 1999, Competitive Models and Game Tree Planning, November 1999 Applications to Economic games. A version published at SSGRR, L'Aquila, Rome, Itlay, September 2001. Invited paper.

40. Cyrus Nourani, F editor 2018 Computing Predictive Analytics, Business Intelligence and Economics, Apple Academic Press. CRC Press: Taylor & Francis.
41. Nourani, C.F. 2005, Business Planning and Cross-Organizational Models, Workshop on Collaborative Cross-Organisational Process Design. Linz, Austria, September 2005 http://www.mensch-und-computer.de/mc2005

42. Cyrus F. Nourani*, Codrina Lauth**, Rasmus Uslev Pedersen Merging Process-Oriented Ontologies with Cognitive Agent, Planning and Attention Spanning, Open Journal of Business Model Innovations, April 2015.

43. Mercier-Laurent, E, 2015, Innovation Eco-Systems and Technology, in Ecosystems and Technology: Idea Generation and Content Model Processing, Chapter 2, Edited volume: Cyrus Nourani editor, Apple Academic Press, Canada-USA.

44. Nourani, C.F., 2002, Multiagent Games, Competitive Models, and Game Tree Planning Fall Symposium, Intent Inference, AAAI, Boston, November 2002 AAAI Press.

45. Cyrus F. Nourani and Oliver Schulte, Multiagent Games, Competitive Models, Descriptive Computing SFU, Vancouver, Canada Draft February 2011, Revision 1:September 25, 2012 SFU, Vancouver, Canada Computation Logic Lab. Europment, April 2014, Prague.