Skip to main content
SpringerLink
Log in

Artificial Cognitive System Architectures

  • Chapter
  • First Online:
Artificial Cognition Architectures

Abstract

Our proposed ACNF, discussed earlier in the book, provides an outline for a possibilistic architecture that can facilitate cognition, learning, memories, and information processing, but it is not solely sufficient to create a comprehensive, autonomous SELF. An overall SELF architecture framework, along with both a knowledge and cognitive framework are required in order to facilitate our fully autonomous, cognitive, self-aware, self-assessing, SELF. We have discussed a SELF system for cognitive management, PENLPE, now we will look at an overall cognitive processing framework, called the Intelligent information Software Agents to facilitate Artificial Consciousness (ISAAC). A SELF architecture, allows dynamic adaptation of the structural elements of the cognitive system, providing abilities to add and prune cognitive elements as necessary as part of SELF evolution [54]. The overall architecture also accommodates a variety of memory classes and algorithmic methods. The basic building blocks of ISAAC comprise an ACNF framework, Cognitron architecture, Fuzzy, Self-Organizing, Semantic Topical Maps (FUSE-SEMs), and a comprehensive Abductive Neural Processing system, the Possibilistic Abductive Neural Network (PANN), for providing consciousness and SELF cognitive functions. Within an ISAAC framework, Cognitrons are added or deleted from the system, based upon the complexity of the classes of information processed. This chapter expounds upon background and architecture for ISAAC, as well as, human-SELF interaction and collaboration, Cognitive, Interactive Training Environment (CITE).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    If H D contains free variables, ∃(H D ) should be consistent w.r.t. B D .

  2. 2.

    Nondeterministic Polynomial time complete. A set or property of computational decision problems which is a subset of NP (i.e. can be solved by a nondeterministic Turing Machine in polynomial time), with the additional property that it is also NP-hard.

  3. 3.

    Note that the general set-covering problem is NP-complete.

  4. 4.

    An abducible argument is a first-order argument consisting of both positive and negative instances of abducable predicates. Abducible predicates are those defined by facts only and the inference engine required to interpret the meaning. In formal logic, abducible refers to incomplete or not completely defined predicates. Problem solving is effected by deriving hypotheses on these abducible predicates as solutions to the problem to be solved (observations to be explained).

References

  1. Bear M, Connors B, Paradiso M (2007) Neuroscience: exploring the brain, 3rd edn. Lippincott, Williams, & Wilkins, Philadelphia, pp 196–199. ISBN 978-0-7817-6003-4

    Google Scholar 

  2. Coutaz J, Crowley J, Dobson S, Garlan D (2005) Context is key. Commun ACM 48:53

    Article  Google Scholar 

  3. Cox PT, Pietrzykowski T (1987) General diagnosis by abductive inference. In: Proceedings of the 1987 symposium on logic programming, San Francisco, pp 183–189

    Google Scholar 

  4. Crowder J (2005) Cognitive systems for data fusion. In: Proceedings of the 2005 PSTN processing technology conference, Ft. Wayne

    Google Scholar 

  5. Crowder J (2006) Intelligent agents for integrated system health management: modern diagnostic and prognostic techniques. In: Proceedings of the air force 3rd annual integrated health management systems conference, Cincinnati

    Google Scholar 

  6. Crowder J (2006) Intelligent information agents. In: Proceedings of the 5th annual system/software symposium, Denver

    Google Scholar 

  7. Crowder J (2007) Integrating metrics with qualitative temporal reasoning for constraint-based expert systems. In: Proceedings of the 2007 processing systems technology network conference, El Segundo

    Google Scholar 

  8. Crowder J, Carbone J (2011) Occam learning through pattern discovery: computational mechanics in AI systems. In: Proceedings of the 13th annual international conference on artificial intelligence, Las Vegas

    Google Scholar 

  9. Crowder J, Friess S (2010) Artificial neural diagnostics and prognostics: self-soothing in cognitive systems. In: Proceedings of the 12th annual international conference on artificial intelligence, Las Vegas

    Google Scholar 

  10. Crowder J, Friess S (2010) Artificial neural emotions and emotional memory. In: Proceedings of the 12th annual international conference on artificial intelligence, ICAI’10

    Google Scholar 

  11. Crowder J, Friess S (2011) The artificial prefrontal cortex: artificial consciousness. In: Proceedings of the 13th annual international conference on artificial intelligence, Las Vegas

    Google Scholar 

  12. Crowder J, Friess S (2011) The artificial prefrontal cortex: artificial consciousness. In: Proceedings of the 12th annual international conference on artificial intelligence, Las Vegas

    Google Scholar 

  13. Crowder J, Friess S (2012) Artificial psychology: the psychology of AI. In: Proceedings of the 3rd annual international multi-conference on informatics and cybernetics. Orlando

    Google Scholar 

  14. Crowder J, Friess S (2012) Warrior resilience training through cognitive self-regulation. In: Proceedings of the 13th annual international conference on artificial intelligence, Las Vegas

    Google Scholar 

  15. Crowder J, Raskin V, Taylor J (2012) Autonomous creation and detection of procedural memory scripts. In: Proceedings of the 13th annual international conference on artificial intelligence, Las Vegas

    Google Scholar 

  16. Hebb D (1949) The organization of behavior. Wiley, New York

    Google Scholar 

  17. Hopfield J (1982) Neural networks and physical systems with emergent collective computational abilities. In: Proceedings of the national academy of science, vol 79

    Google Scholar 

  18. Humberto R, Varela F (1992) The tree of knowledge: the biological roots of human understanding. Shambhala Publications, Boston

    Google Scholar 

  19. Igor A, Antonova I, Kandel E, Hawkins R (2003) Activity-dependent presynaptic facilitation and hebbian LTP are both required and interact during classical conditioning in Aplysia

    Google Scholar 

  20. Kahana M, Howard M, Polyn S (2008) Associative retrieval processes in episodic memory. In: Roediger HL III (ed) Cognitive psychology of memory, vol 2 of learning and memory: a comprehensive reference, 4 vols. (Byrne J, ed). Elsevier, Oxford

    Google Scholar 

  21. LeDoux J (2002) Synaptic self: how our brains become who we are. Viking, New York

    Google Scholar 

  22. Lehtonen M (2012) Long-term phonological learning begins at the level of word form. NeuroImage

    Google Scholar 

  23. Levine P (1997) Walking the tiger: healing trauma. North Atlantic Books, Berkeley

    Google Scholar 

  24. Newell A, Shaw J, Simon H (1957) Preliminary description of general problem solving program-i (gps-i). WP7, Carnegie Institute of Technology, Pittsburgh

    Google Scholar 

  25. Taylor JM, Raskin V (2011) Understanding the unknown: unattested input processing in natural language, FUZZ-IEEE conference, Taipei

    Google Scholar 

  26. Landauer T, Dumas S (1997) Solution to Plato’s problem: the latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychol Rev 194:211–240

    Article  Google Scholar 

  27. Carbone J (2010) Framework for enhancing transdisciplinary research knowledge. Tech University Press, Texas

    Google Scholar 

  28. Fishburn P (1989) Retrospective on the utility theory of Von Neumann and Morgenstern. J Risk Uncertain 2:127–157

    Article  MathSciNet  Google Scholar 

  29. Machina M (1982) Expected utility: analysis without the Independence Axiom. Econometrica 50:277–323

    Article  MathSciNet  MATH  Google Scholar 

  30. Akao Y (2004) Quality function deployment: integrating customer requirements into product design. Productivity PR

    Google Scholar 

  31. Suh N (2005) Complexity theory and applications. Oxford University Press, New York

    Google Scholar 

  32. Kandel E, Schwartz J, Jessel T The anatomical organization of the central nervous system. Principles of neural science (Ch. 17), McGraw Hill Professional, ISBN 978-0-8383-7701-1

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Englewood, CO, USA

    James A. Crowder

  2. Raytheon Intelligence and Information Systems, McKinney, TX, USA

    John N. Carbone

  3. Relevant Counseling LLC, Englewood, CO, USA

    Shelli A. Friess

Authors
  1. James A. Crowder
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John N. Carbone
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shelli A. Friess
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this chapter

Cite this chapter

Crowder, J.A., Carbone, J.N., Friess, S.A. (2014). Artificial Cognitive System Architectures. In: Artificial Cognition Architectures. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8072-3_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-8072-3_9

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-8071-6

  • Online ISBN: 978-1-4614-8072-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation