A Framework for Modeling and Simulation of the Artificial

Artificial systems that generate contingency-based teleological behaviors in real-time, are difficult to model. This chapter describes a modeling and simulation (M&S) framework designed specifically to reduce this difficulty. The described Knowledge-based Contingency-driven Generative Systems (KCGS) framework combines aspects of SES theory, DEVS-based general systems theory, net-centric heterogeneous simulation, knowledge engineering, cognitive modeling, and domain-specific language development using meta-modeling. The chapter outlines the theoretical and technical foundations of the KCGS framework as realized in the Cognitive Systems Specification Framework (CS2F), a subset of KCGS. Two executable models are described to illustrate how models of autonomous, goalpursuing cognitive systems can be modeled and simulated in the framework. The technical content and agent descriptions in the chapter illustrate how the M&S of the artificial depends critically on ontology, epistemology, and teleology in the KCGS framework.

[1]  Bernard P. Zeigler,et al.  Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems , 2000 .

[2]  Jennifer K. Phillips,et al.  A Data–Frame Theory of Sensemaking , 2007 .

[3]  S. Douglass,et al.  Concurrent Knowledge Activation Calculation in Large Declarative Memories , 2010 .

[4]  Bernard P. Zeigler,et al.  Reachability Graph of Finite and Deterministic DEVS Networks , 2009, IEEE Transactions on Automation Science and Engineering.

[5]  Yueh-Min Huang,et al.  Rule-Based Generation of Model Structures in Multifaceted Modeling and System Design , 1991, INFORMS J. Comput..

[6]  Saurabh Mittal,et al.  Netcentric System of Systems Engineering with DEVS Unified Process , 2013 .

[7]  Gabriel A. Wainer,et al.  Discrete-Event Modeling and Simulation : Theory and Applications , 2010 .

[8]  Bernard P. Zeigler,et al.  DEVSML: automating DEVS execution over SOA towards transparent simulators , 2007, SpringSim '07.

[9]  Dirk Ifenthaler,et al.  Computer-Based Diagnostics and Systematic Analysis of Knowledge , 2010 .

[10]  Deborah L. McGuinness,et al.  OWL Web ontology language overview , 2004 .

[11]  Jr. Guy L. Steele,et al.  Common LISP: the language (2nd ed.) , 1990 .

[12]  Gabor Karsai,et al.  Model-Integrated Computing , 1997, Computer.

[13]  Margaret Wilson,et al.  Six views of embodied cognition , 2002, Psychonomic bulletin & review.

[14]  Bernard P. Zeigler,et al.  Knowledge-Based Design and Simulation Environment (KBDSE): Foundational Concepts and Implementation , 1990 .

[15]  Trevor Cohen,et al.  Abductive Reasoning and Similarity: Some Computational Tools , 2010 .

[16]  Ákos Lédeczi,et al.  An Introduction to the Generic Modeling Environment , 2007 .

[17]  Bernard P. Zeigler,et al.  Implementation of a Formal Standard for Interoperability in M & S / Systems of Systems Integration with DEVS / SOA , 2008 .

[18]  Bernard P. Zeigler,et al.  System entity structuring and model base management , 1990, IEEE Trans. Syst. Man Cybern..

[19]  Saurabh Mittal,et al.  Using Domain-Specific Languages to Improve the Scale and Integration of Cognitive Models , 2011 .

[20]  Gabor Karsai,et al.  The Generic Modeling Environment , 2001 .

[21]  Cleotilde Gonzalez,et al.  Instance-based learning in dynamic decision making , 2003, Cogn. Sci..

[22]  S. White,et al.  Constraint Handling in Common LISP , 2007 .

[23]  Bernard P. Zeigler,et al.  SES-based ontological process for high level information fusion , 2010, SpringSim.

[24]  David E. Kieras,et al.  An Overview of the EPIC Architecture for Cognition and Performance With Application to Human-Computer Interaction , 1997, Hum. Comput. Interact..

[25]  Arthur W. Burks,et al.  Peirce's Theory of Abduction , 1946, Philosophy of Science.

[26]  John R Anderson,et al.  An integrated theory of the mind. , 2004, Psychological review.

[27]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

[28]  David A. McAllester,et al.  Screamer: A Portable Efficient Implementation of Nondeterministic Common LISP , 1993 .

[29]  Saurabh Mittal,et al.  Net-centric act-R-based cognitive architecture with DEVS unified process , 2011, SpringSim.

[30]  Bernard P. Zeigler,et al.  DEVS/SOA: A Cross-Platform Framework for Net-centric Modeling and Simulation in DEVS Unified Process , 2009, Simul..

[31]  José Luis Risco-Martín,et al.  Interoperability between DEVS and non-DEVS models using DEVS/SOA , 2009, SpringSim '09.

[32]  Xiaolin Hu,et al.  Towards a Formal Standard for Interoperability in M & S / System of Systems Integration , 2008 .

[33]  Bernard P. Zeigler,et al.  Devs unified process for integrated development and testing of service oriented architectures , 2007 .

[34]  Francesco Cesarini,et al.  Erlang Programming , 2009 .

[35]  Richard Reviewer-Granger.  Unified Theories of Cognition , 1991, Journal of Cognitive Neuroscience.

[36]  John R. Anderson.  How Can the Human Mind Occur in the Physical Universe , 2007 .

[37]  Bernard P. Zeigler,et al.  DEVS-based simulation web services for net-centric T&E , 2007, SCSC.

[38]  Saurabh Mittal,et al.  Agile Net-Centric Systems Using DEVS Unified Process , 2011 .

[39]  Bernard P. Zeigler,et al.  Modeling & Simulation-Based Data Engineering: Introducing Pragmatics into Ontologies for Net-Centric Information Exchange , 2007 .

[40]  Bernard P. Zeigler,et al.  Model base management for multifacetted systems , 1991, TOMC.

[41]  Bernard P. Zeigler,et al.  System Entity Structure Ontological Data Fusion Process Integrated with C2 Systems , 2010 .

[42]  Saurabh Mittal,et al.  DEVSML 2.0: the language and the stack , 2012, SpringSim.

[43]  Bernard P. Zeigler,et al.  Representing and constructing system specifications using the system entity structure concepts , 1993, WSC '93.

[44]  Bernard P. Zeigler,et al.  Model-based architecture concepts for autonomous systems design and simulation , 1993 .

[45]  Saurabh Mittal,et al.  From domain specific languages to DEVS components: application to cognitive M&S , 2011, SpringSim.