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Integrated cognitive architectures: a survey

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Abstract

This article aims to present an account of the state of the art research in the field of integrated cognitive architectures by providing a review of six cognitive architectures, namely Soar, ACT-R, ICARUS, BDI, the subsumption architecture and CLARION. We conduct a detailed functional comparison by looking at a wide range of cognitive components, including perception, memory, goal representation, planning, problem solving, reasoning, learning, and relevance to neurobiology. In addition, we study the range of benchmarks and applications that these architectures have been applied to. Although no single cognitive architecture has provided a full solution with the level of human intelligence, important design principles have emerged, pointing to promising directions towards generic and scalable architectures with close analogy to human brains.

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References

  • Amir E, Maynard-Zhang P (2004) Logic-based subsumption architecture. Artif Intell 153: 167–237

    Article  MATH  MathSciNet  Google Scholar 

  • Anderson M (2003) Embodied cognition: a field guide. Artif Intell 149: 91–130

    Article  Google Scholar 

  • Anderson J, Schunn C (2000) Implications of the ACT-R learning theory: No magic bullets. Adv Instr Psychol 5: 1–34

    Google Scholar 

  • Anderson JR, Bothell D, Byrne MD, Douglass S, Lebiere C, Qin Y (2004) An intergrated theory of the mind. Psychol Rev 111: 1036–1060

    Article  Google Scholar 

  • Bratman M, Israel D, Pollack M (1988) Plans and resource-bounded practical reasoning. Comput Intell 4(4): 349–355

    Article  Google Scholar 

  • Braubach L, Pokahr A, Moldt D, Lamersdorf W (2005) Goal representation for BDI agent systems. In: Proceedings, 2nd international workshop on programming multiagent systems: languages and tools, pp 9–20

  • Brooks RA (1991) How to build complete creatures rather than isolated cognitive simulators. In: Proceedings, architectures for intelligence, pp 225–240

  • Brooks R (1999) Cambrian intelligence: the early history of the new AI. MIT Press, Boston, MA

    MATH  Google Scholar 

  • Bryson J, Smaill A, Wiggins G (2005) The reactive accompanist: applying subsumption architecture to software design. Tech. rep., Department of Artificial Intelligence, University of Edinburgh

  • Butler G, Gantchev A, Grogona P (2001) Object-oriented design of the subsumption architecture. Softw Pract Exper 31: 911–923

    Article  MATH  Google Scholar 

  • Chong RS (2004) Architectural explorations for modeling procedural skill decay. In: Proceedings, sixth international conference on cognitive modeling

  • Dastani M, van der Torre L (2002) An extension of BDI CTL with functional dependencies and components. In: Proceedings, 9th international conference on logic for programming, artificial intelligence, and reasoning, pp 115–129

  • Dastani M, Hulstijn J, van der Torre L (2001) BDI and QDT: a comparison based on classical decision theory. In: Proceedings, AAAI symposium, pp 16–26

  • Dennett D (1987) The intentional stance. MIT Press, Cambridge MA

    Google Scholar 

  • Georgeff M, Ingrand F (1989) Decision-making in an embedded reasoning system. In: Proceedings, international joint conference on artificial intelligence, pp 972–978

  • Guerra-Hernandez A, Fallah-Seghrouchni AE, Soldano H (2004) Learning in BDI multi-agent systems. In: Proceedings, 4th international workshop on computational logic in multi-agent systems. Fort Lauderdale, FL

  • Hartley R, Pipitone F (1991) Experiments with the subsumption architecture. In: Proceedings, IEEE international conference on robotics and automation, pp 1652–1658

  • Köse H (2000) Towards a robust cognitive architecture for autonomous mobile robots. Master’s thesis, Boğaziçi University

  • Kopp S, Gesellensetter L, Krämer N, Wachsmuth I (2005) A conversational agent as museum guide—design and evaluation of real-world application. Intell Virtual Agents 5: 329–343

    Article  Google Scholar 

  • Laird J, Newell A, Rosenbloom P (1987) SOAR: An architecture for general intelligence. Artif Intell 33(1): 1–64

    Article  MathSciNet  Google Scholar 

  • Laird J, Rosenbloom P, Newell A (1986a) Chunking in Soar: The anatomy of a general learning mechanism. Mach Learn 1: 11–46

    Google Scholar 

  • Laird J, Rosenbloom P, Newell A (1986b) Universal subgoaling and chunking: the automatic generation and learning of goal hierarchies. Kluwer, Boston, MA

    Google Scholar 

  • Langley P (2004) A cognitive architecture for physical agents. Retrieved 28 Oct 2006 from http://www.isle.org/~langley/talks/icarus.6.04.ppt

  • Langley P, Choi D (2006) A unified cognitive architecture for physical agents. In: Proceedings, twenty-first national conference on artificial intelligence, pp 1469–1474

  • Langley P, Arai S, Shapiro D (2004) Model-based learning with hierarchical relational skills. In: Proceedings, ICML-2004 workshop on relational reinforcement learning

  • Lehman J, Laird J, Rosenbloom P (2006) A gentle introduction to soar, an architcture for human cognition: 2006 update. Retrieved 17 May 2007 from http://ai.eecs.umich.edu/soar/sitemaker/docs/misc/GentleIntroduction-2006.pdf

  • Madden M, Howley T (2003) Transfer of experience between reinforcement learning environments with progressive difficulty. Artif Intell Rev 21(3–4): 375–398

    Google Scholar 

  • Nakashima H, Noda I (1998) Dynamic subsumption architecture for programming intelligent agents. In: Proceedings, IEEE international conference on multi-agent systems, pp 190–197

  • Newell A (1990) Unified theories of cognition. Harvard University Press, Cambridge, MA

    Google Scholar 

  • Norling E (2004) Folk psychology for human modelling: extending the BDI paradigm. In: Proceedings, international joint conference on autonomous agents and multi-agent systems (AAMAS’04), pp 202–209

  • Nuxoll A, Laird J (2004) A cognitive model of episodic memory integrated with a general cognitive architecture. In: Proceedings, sixth international conference on cognitive modeling, pp 220–225

  • Nuxoll A, Laird J, James M (2004) Comprehensive working memory activation in soar. In: Proceedings, sixth international conference on cognitive modeling, pp 226–230

  • Rao AS, Georgeff MP (1991) Modeling rational agents within a bdi-architecture. In: Proceedings, second international conference on principles of knowledge representation and reasoning. Morgan Kaufmann, San Mateo, CA, pp 473–484

  • Rickel J, Johnson W (2000) Task-oriented collaboration with embodied agents in virtual worlds. In: Cassell J, Sullivan J, Prevost S(eds) Embodied conversational agents. MIT Press, Boston, pp 95–122

    Google Scholar 

  • Sardina S, de Silva L, Padgham L (2006) Hierarchical planning in BDI agent programming languages: a formal approach. In: Proceedings, autonomous agents and multi-agent systems (AAMAS), pp 1001–1008

  • Si J, Barto AG, Powell WB, Wunsch D (eds) (2004) Handbook of learning and approximate dynamic programming. Wiley-IEEE Press, New York, NY

  • Stollberg M, Rhomberg F (2006) Survey on goal-driven architectures, Tech. rep., DERI, Austria

  • Subagdja B, Sonenberg L (2005) Learning plans with patterns of actions in bounded-rational agents. In: Proceedings, 9th international conference on knowledge-based and intelligent information & engineering systems (KES’05), vol 3, pp 30–36

  • Sun R (2003) A tutorial on CLARION 5.0. Tech. rep., Cognitive Science Department, Rensselaer Polytechnic Institute

  • Sun R, Peterson T (1996) Learning in reactive sequential decision tasks: the CLARION model. In: Proceedings, IEEE international conference on neural networks, pp 1073–1078

  • Sun R, Peterson T (1998) Hybrid learning incorporating neural and symbolic processes. In: Proceedings, IEEE international conference on fuzzy systems, pp 727–732

  • Sun R, Sessions C (1998) Learning to plan probabilistically from neural networks. In: Proceedings of IEEE international conference on neural networks, pp 1–6

  • Sun R, Zhang X (2006) Accounting for a variety of reasoning data within a cognitive architecture. J Exp Theor Artif Intell 18(2): 169–191

    Article  Google Scholar 

  • Sun R, Merrill E, Peterson T (2001) From implicit skills to explicit knowledge: a bottom-up model of skill learning. Cogn Sci 25(2): 203–244

    Article  Google Scholar 

  • Sun R, Slusarz P, Terry C (2005) The interaction of the explicit and the implicit in skill learning: a dual-process approach. Psychol Rev 112(1): 159–192

    Article  Google Scholar 

  • Sutton R, Barto A (1998) Reinforcement learning: an introduction. MIT Press, Cambridge, MA

    Google Scholar 

  • Swartout W, Gratch J, Hill REH, Marsella S, Rickel J, Traum D (2006) Toward virtual humans. Artif Intell Mag 27(2): 96–108

    Google Scholar 

  • Tan A-H (2004) FALCON: a fusion architecture for learning, cognition, and navigation. In: Proceedings, international joint conference on neural networks, pp 3297–3302

  • Tan A-H (2007) Direct code access in self-organizing neural networks for reinforcement learning. In: Proceedings, international joint conference on artificial intelligence (IJCAI’07), pp 1071–1076

  • Tan A-H, Carpenter G A, Grossberg S (2007) Intelligence through interaction: towards a unified theory for learning. In: Proceedings, international symposium on neural networks (ISNN’07), LNCS4491, pp 1098–1107

  • Tan A-H, Lu N, Xiao D (2008) Integrating temporal difference methods and self-organizing neural networks for reinforcement learning with delayed evaluative feedback. IEEE Trans Neural Netw 9(2):230–244

    Google Scholar 

  • Toal D, Flanagan C, Jones C, Strunz B (1996) Subsumption architecture for the control of robots. In: IMC-13

  • Vernon D, Metta G, Sandini G (2007) A survey of artificial cognitive systems: implications for the autonomous development of mental capabilities in computational agents. IEEE Trans Evol Comput 11(2): 151–180

    Article  Google Scholar 

  • Watkins C, Dayan P (1992) Q-learning. Mach Learn 8(3/4): 279–292

    Article  MATH  Google Scholar 

  • West R, Stewart T, Lebiere C, Chandrasekharan S (2005) Stochastic resonance in human cognition: ACT-R versus game theory, associative neural networks, recursive neural networks, Q-learning, and humans. In: 27th annual meeting of the cognitive science society

  • Wooldridge M (1996) A logic for BDI planning agents. In: Working notes, 3rd model age workshop: formal models of agents

  • Wray R, Chong R, Phillips J, Rogers S, Walsh B (1994) A survey of cognitive and agent architectures. Retrieved 28 Jan 2007 from http://ai.eecs.umich.edu/cogarch0/

  • Xiao D, Tan A-H (2007) Self-organizing neural architectures and cooperative learning in multi-agent environment. IEEE Trans Syst Man Cybern B 37(6): 1567–1580

    Article  Google Scholar 

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Chong, HQ., Tan, AH. & Ng, GW. Integrated cognitive architectures: a survey. Artif Intell Rev 28, 103–130 (2007). https://doi.org/10.1007/s10462-009-9094-9

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