Integrated cognitive architectures: A survey

Artificial Intelligence Review

Volumn 28, Issue 2, 2007, Pages 103-130

  Chong, Hui Qing ^a   Tan, Ah Hwee ^b   Ng, Gee Wah ^c  

^a Nanyang Technological University   (Singapore)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c DSO NATIONAL LABORATORIES   (Singapore)

Author keywords

ACT R; BDI; CLARION; ICARUS; Integrated cognitive architectures; Soar; Subsumption architecture

Indexed keywords

BENCHMARKING;

ACT-R; BDI; CLARION; ICARUS; INTEGRATED COGNITIVE ARCHITECTURES; SOAR; SUBSUMPTION ARCHITECTURE;

ARCHITECTURAL DESIGN;

EID: 62249199071     PISSN: 02692821     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10462-009-9094-9     Document Type: Article

References (58)

1. E Amir P Maynard-Zhang 2004 Logic-based subsumption architecture Artif Intell 153 167 237
2. M Anderson 2003 Embodied cognition: a field guide Artif Intell 149 91 130
3. J Anderson C Schunn 2000 Implications of the ACT-R learning theory: No magic bullets Adv Instr Psychol 5 1 34
4. JR Anderson D Bothell MD Byrne S Douglass C Lebiere Y Qin 2004 An intergrated theory of the mind Psychol Rev 111 1036 1060
5. M Bratman D Israel M Pollack 1988 Plans and resource-bounded practical reasoning Comput Intell 4 4 349 355
6. 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
7. Brooks RA (1991) How to build complete creatures rather than isolated cognitive simulators. In: Proceedings, architectures for intelligence, pp 225-240
8. Brooks R (1999) Cambrian intelligence: the early history of the new AI. MIT Press, Boston, MA
9. 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
10. G Butler A Gantchev P Grogona 2001 Object-oriented design of the subsumption architecture Softw Pract Exper 31 911 923
11. Chong RS (2004) Architectural explorations for modeling procedural skill decay. In: Proceedings, sixth international conference on cognitive modeling
12. CTL with functional dependencies and components. In: Proceedings, 9th international conference on logic for programming, artificial intelligence, and reasoning, pp 115-129
13. 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
14. Dennett D (1987) The intentional stance. MIT Press, Cambridge MA
15. Georgeff M, Ingrand F (1989) Decision-making in an embedded reasoning system. In: Proceedings, international joint conference on artificial intelligence, pp 972-978
16. 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
17. Hartley R, Pipitone F (1991) Experiments with the subsumption architecture. In: Proceedings, IEEE international conference on robotics and automation, pp 1652-1658
18. Köse H (2000) Towards a robust cognitive architecture for autonomous mobile robots. Master's thesis, Boǧaziçi University
19. S Kopp L Gesellensetter N Krämer I Wachsmuth 2005 A conversational agent as museum guide-design and evaluation of real-world application Intell Virtual Agents 5 329 343
20. J Laird A Newell P Rosenbloom 1987 SOAR: An architecture for general intelligence Artif Intell 33 1 1 64
21. J Laird P Rosenbloom A Newell 1986 Chunking in Soar: The anatomy of a general learning mechanism Mach Learn 1 11 46
22. Laird J, Rosenbloom P, Newell A (1986b) Universal subgoaling and chunking: the automatic generation and learning of goal hierarchies. Kluwer, Boston, MA
23. Langley P (2004) A cognitive architecture for physical agents. Retrieved 28 Oct 2006 from http://www.isle.org/~langley/talks/icarus.6.04.ppt
24. Langley P, Choi D (2006) A unified cognitive architecture for physical agents. In: Proceedings, twenty-first national conference on artificial intelligence, pp 1469-1474
25. Langley P, Arai S, Shapiro D (2004) Model-based learning with hierarchical relational skills. In: Proceedings, ICML-2004 workshop on relational reinforcement learning
26. 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
27. M Madden T Howley 2003 Transfer of experience between reinforcement learning environments with progressive difficulty Artif Intell Rev 21 3-4 375 398
28. Nakashima H, Noda I (1998) Dynamic subsumption architecture for programming intelligent agents. In: Proceedings, IEEE international conference on multi-agent systems, pp 190-197
29. Newell A (1990) Unified theories of cognition. Harvard University Press, Cambridge, MA
30. 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
31. 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
32. Nuxoll A, Laird J, James M (2004) Comprehensive working memory activation in soar. In: Proceedings, sixth international conference on cognitive modeling, pp 226-230
33. 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
34. 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
35. 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
36. Si J, Barto AG, Powell WB, Wunsch D (eds) (2004) Handbook of learning and approximate dynamic programming. Wiley-IEEE Press, New York, NY
37. Stollberg M, Rhomberg F (2006) Survey on goal-driven architectures, Tech. rep., DERI, Austria
38. 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
39. Sun R (2003) A tutorial on CLARION 5.0. Tech. rep., Cognitive Science Department, Rensselaer Polytechnic Institute
40. 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
41. Sun R, Peterson T (1998) Hybrid learning incorporating neural and symbolic processes. In: Proceedings, IEEE international conference on fuzzy systems, pp 727-732
42. Sun R, Sessions C (1998) Learning to plan probabilistically from neural networks. In: Proceedings of IEEE international conference on neural networks, pp 1-6
43. R Sun X Zhang 2006 Accounting for a variety of reasoning data within a cognitive architecture J Exp Theor Artif Intell 18 2 169 191
44. R Sun E Merrill T Peterson 2001 From implicit skills to explicit knowledge: a bottom-up model of skill learning Cogn Sci 25 2 203 244
45. R Sun P Slusarz C Terry 2005 The interaction of the explicit and the implicit in skill learning: a dual-process approach Psychol Rev 112 1 159 192
46. Sutton R, Barto A (1998) Reinforcement learning: an introduction. MIT Press, Cambridge, MA
47. W Swartout J Gratch REH Hill S Marsella J Rickel D Traum 2006 Toward virtual humans Artif Intell Mag 27 2 96 108
48. Tan A-H (2004) FALCON: a fusion architecture for learning, cognition, and navigation. In: Proceedings, international joint conference on neural networks, pp 3297-3302
49. 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
50. 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
51. 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
52. Toal D, Flanagan C, Jones C, Strunz B (1996) Subsumption architecture for the control of robots. In: IMC-13
53. D Vernon G Metta G Sandini 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
54. C Watkins P Dayan 1992 Q-learning Mach Learn 8 3/4 279 292
55. 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
56. Wooldridge M (1996) A logic for BDI planning agents. In: Working notes, 3rd model age workshop: formal models of agents
57. 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/
58. D Xiao A-H Tan 2007 Self-organizing neural architectures and cooperative learning in multi-agent environment IEEE Trans Syst Man Cybern B 37 6 1567 1580