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Modular Models of Intelligence – Review, Limitations and Prospects

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Abstract

AI applications are increasingly moving to modular agents, i.e.,systems that independently handle parts of the problem based on smalllocally stored information (Grosz and Davis 1994), (Russell and Norvig 1995). Many suchagents minimize inter-agent communication by relying on changes in theenvironment as their cue for action. Some early successes of thismodel, especially in robotics (``reactive agents''), have led to adebate over this class of models as a whole. One of theissues on which attention has been drawn is that of conflicts betweensuch agents. In this work we investigate a cyclic conflict thatresults in infinite looping between agents and has a severedebilitating effect on performance. We present some new results inthe debate, and compare this problem with similar cyclicity observedin planning systems, meta-level planners, distributed agent models andhybrid reactive models. The main results of this work are:

(a) The likelihood of such cycles developing increasesas the behavior sets become more useful.(b) Control methods for avoiding cycles such asprioritization are unreliable, and(c) Behavior refinement methods that reliably avoidthese conflicts (either by refining the stimulus, or by weakeningthe action) lead to weaker functionality.

Finally, we show how attempts to introduce learning into thebehavior modules will also increase the likelihood of cycles.

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References

  • Abonamah A. A. and Elmagarmid A. K. (1994). A survey of deadlock detection algorithms in distributed database systems. In Tyrer H. W. (ed.) Advances in distributed and parallel processing - system paradigms and methods 1, 310-341. Ablex publishing corp.

  • Anderson T. L. and Donath M. (1990). Animal Behavior As A Paradigm For Developing Robot Autonomy, Robotics and Autonomous Systems 6(1,2): 145–168.

    Google Scholar 

  • Arkin R. C. (1992). Behavior-Based Robot Navigation for Extended Domains, Adaptive Behavior 1(2): 201–225.

    Google Scholar 

  • Brooks R. A. (1986). A robust layered control system for a mobile robot, IEEE journal on robotics and automation 2(1): 14–23.

    Google Scholar 

  • Chu-Carroll Jennifer and Carberry Sandra (1995). Communication for Conflict Resolution in Multi-Agent Collaborative Planning, Proceedings of the First Int'l Conference on Multiagent Systems (ICMAS), 49-56.

  • Congdon Clare, Huber Marcus, Kortenkamp David, Konolige Kurt, Myers Karen, Saffiotti Alessandro and Ruspini Enrique H. (1993). CARMEL versus FLAKEY: A comparison of two winners, AI Magazine, Spring issue: 49-57.

  • Connell J. (1990). Minimalist mobile robotics, A colony style architecture for an artificial creature, Academic press Inc.

  • Corkill Daniel D. and Lesser Victor R. (1983). The use of meta-level control for coordination in a distributed problem solving network, Proc. of International Joint Conference on Artificial Intelligence (IJCAI), 748-756.

  • Durfee E. H. (1992). What Your Computer Really Needs to Know You Learned in Kindergarten. In Proc. of the National Conference on Artificial Intelligence (AAAI), San Jose, CA: 858-864.

  • Durfee Edmund H., Kenny Patrick G. and Kluge Karl C. (1998). Integrated Permission Planning and Execution for Unmanned Ground Vehicles, Autonomous Robots 5: 1–14.

    Google Scholar 

  • Foster Ian (1995). Designing and building parallel programs, Addison-Wesley.

  • Gat E. (1993). On the Role of Stored Internal State in the Control of Autonomous Mobile Robots, AI Magazine 14(1): 64–73.

    Google Scholar 

  • Georgeff M. P. and Lansky A. L. (1990). Reactive Reasoning and Planning. In James Allen, James Hendler and Austin Tate (eds.) Readings In Planning, 729-734. Morgan Kaufmann Publishers, Inc.

  • Grosz Barbara J. and Davis Randall (1994). AAAI Report to ARPA on 21st century intelligent systems, AI Magazine Fall issue: 10-20.

  • Hammond Kristian J. and Converse Timothy M. (1991). Stabilizing environments to facilitate planning and activity: An engineering argument, Proceedings of the National Conference on Artificial Intelligence (AAAI) 2.

  • Hartley R. and Pipitone F. (1991). Experiments with the subsumption architecture, In Proceedings of the IEEE Conference on Robotics and Automation (ICRA), 1652-1658.

  • Hickman Stephen and Shiels Martin (1991). Situated action as a basis for cooperation. In Yves Demazeau and Jean-Pierre Muller (eds.) Decentralized AI 2, 35-47. Elsevier Science Publishers B. V.

  • Jennings N. R. (1995). Controlling cooperative problem solving in industrial multi-agent systems using joint intentions, Artificial Intelligence 75: 195–240.

    Google Scholar 

  • Kirsh, D. (1991). Today the earwig, tomorrow man?, Artificial Intelligence 47(1-3): 161–184.

    Google Scholar 

  • Konolige Kurt (1994). Designing the 1993 robot competition, AI Magazine Spring issue: 57-62.

  • Kortenkamp David, Huber Marcus, Choen Charles, Raschke Ulrich, Bidlack Clint, Congdon Clare Bates, Koss Frank and Weymouth Terry (1993). Integrated mobile robot design: Winning the AAAI'92 robot competition, IEEE Expert, August issue: 61-73.

  • Kube C. Ronald and Zhang Hong (1997). Task modeling in collective robotics, Autonomous Robots 4, 53–72.

    Google Scholar 

  • Laird John and Rosenbloom Paul (1990). Integrating execution, planning and learning in Soar for external environments, Proceedings of the National Conference on Artificial Intelligence (AAAI), 1022-1029.

  • Lenat D. B., Guha R. V., Pittman K., Pratt D. and Shepherd M. (1990). CYC: towards programs with common sense, Communications of the ACM, August issue 33(8): 30–49

    Google Scholar 

  • Mahadevan S. and Connell J. (1992). Automatic programming of behavior-based robots using reinforcement learning, Artificial Intelligence 55, 311–365.

    Google Scholar 

  • Masthoff J. and Hoe Van R. (1995). A View on the Architecture and Design of Highly Autonomous and Situated Agents, Proceedings of the First International Conference on Multiagent Systems (ICMAS) Victor Lesser: 458 (Poster).

  • Mataric Maja J. (1997). Using communication to reduce locality in multi-robot learning, Proceedings of the National Conference on Artificial Intelligence (AAAI): 643-648.

  • Mitchell Tom (1990). Becoming increasingly reactive, Proceedings of the National Conference on Artificial Intelligence (AAAI), 1051-1058.

  • Moravec H. P. (1984). Locomotion, Vision, and Intelligence, Proceedings of the First International Symposium on Robotics Research, Bretton Woods, NH, edited by Michael Brady and Richard Paul, MIT Press, Cambridge, MA, 215–224.

    Google Scholar 

  • Muscettola Nicola, Nayak Pandurang P., Pell Barney and Williams Brian C. (1998). Remote agent: To boldly go where no AI system has gone before, Artificial Intelligence 103(1-2): 5–47.

    Google Scholar 

  • Nicolescu Monica N. and Mataric Maja (2000). Deriving and using abstract representation in behavior-based system, Proceedings of the National Conference on Artificial Intelligence (AAAI) Student abstract: 1087.

  • Parker Lynne E. (1996). On the design of behavior-based multi-robot teams, Advanced Robotics 10(6): 547–578.

    Google Scholar 

  • Payton, D. W., Rosenblatt J. K. and Keirsey D. M. (1990). Plan guided reaction, IEEE Transactions on Systems, Man and Cybernetics 20(6): 1370–1382.

    Google Scholar 

  • Reiter R. (1991). The frame problem in the situation calculus: A simple solution (sometimes) and a completeness result for goal regression. In Lifschitz V. (ed.) Artificial Intelligence and Mathematical Theory of Computation: Papers in Honor of John McCarthy 359–380. Academic Press, NY.

    Google Scholar 

  • Rish Irina and Dechter Rina (1996). To guess or to think? Hybrid algorithms for SAT, Proceedings of the Principles and Practices of Constraint Programming (PPCP).

  • Hayes-Roth Frederick (1996). AI:What works and what doesn't? Invited talk at the Innovative Applications of Artificial Intelligence conference (IAAI), Portland.

  • Russell Stuart and Norvig Peter (1995). Artificial Intelligence: A Modern Approach, Prentice-Hall, NJ.

    Google Scholar 

  • Samadi Behrokh and Muntz Richard (1988). A distributed algorithm to detect a global state of a distributed simulation system, In Barton M. H., Dagless E. L. and Reijns G. L. (eds.) Distributed processing, 19-34. North-Holland.

  • Schaerf Andrea (1997). Combining local search and look-ahead for scheduling and constraint satisfaction problems, Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 1254-1259.

  • Simmons Reid (1994). Structured control for autonomous robots, IEEE Transactions Robotics and Automation February issue.

  • Simmons Reid, Apfelbaum David, Burgard Wolfram, Fox Dieter, Moors Mark, Thrun Sebastian and Younes Hakan, (2000). Coordination for multi-robot exploration and mapping, Proceedings of the National Conference on Artificial Intelligence (AAAI), 852-858.

  • Stone Peter, Riley Patrick and Veloso Manuela (2000). Defining and using ideal template and opponent agent models, Proceedings of the National Conference on Artificial Intelligence (AAAI), 1040-1045.

  • Tambe Milind (2000). Agent assistants for team analysis, AI Magazine Fall issue: 27-31.

  • Vera A. H. and Simon Herbert A. (1993). Situated Action: A Symbolic Interpretation, Cognitive Science 17: 7–48.

    Google Scholar 

  • Wellman Michael P. (1992). A general-equillibrium approach to distributed transportation planning, Proceedings of the National Conference on Artificial Intelligence (AAAI), 282-289.

  • Winner Elly and Veloso Manuela (2000). Multi-fidelity robotic behaviors: Acting with variable state information, Proceedings of the National Conference on Artificial Intelligence (AAAI), 872-877.

  • Wooldridge Michael and Jennings Nick (1995). Agent Theories, Architectures, and Languages: A Survey, In Michael Wooldridge and Nicholas R. Jennings (ed.) Intelligent Agents - Theories, Architectures, and Languages, 1-32. Springer-Verlag Lecture Notes in Artificial Intelligence January issue.

  • Yiu Leo and Shyamsundar R. K. (1990). Static analysis of deadlock, distributed termination and timing properties in real-time distributed systems, In Cosnard M. and Girault C. (eds.) Decentralized systems 411-425. Elsevier science publishers B. V. (North-Holland).

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Authors
  1. Amitabha Mukerjee
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  2. Amol Dattatraya Mali
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Mukerjee, A., Mali, A.D. Modular Models of Intelligence – Review, Limitations and Prospects. Artificial Intelligence Review 17, 39–64 (2002). https://doi.org/10.1023/A:1015098212815

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