Abstract
Open environments are characterized by their uncertainty and non-determinism. Agents need to adapt their task processing to available resources, deadlines, the goal criteria specified by the clients as well their current problem solving context in order to survive in these environments. If there were no resource constraints, then an optimal Markov Decision Process based policy would obviously be the best way for complex problem solving agents to make scheduling decisions. However in many agent systems, these scheduling decisions have to be made on-line or in soft real-time, making the off-line policy computationally infeasible in open environments. The hybrid planner/scheduler used to control Task Analysis, Environment Modeling, and Simulation (TÆMS) agents is the Design-to-Criteria (DTC) agent scheduler. Design-to-Criteria scheduling is the soft real-time process of custom building a plan/schedule to meet an agent’s current objectives which are expressed as dynamic goal criteria (including real-time deadlines), using task models that describe alternate ways to achieve tasks and subtasks. Recent advances in Design-to-Criteria control include the addition of uncertainty to the TÆMS computational task models analyzed by the scheduler and the incorporation of uncertainty in the scheduling process. As we show, the use of uncertainty in TÆMS and Design-to-Criteria enables agents to make better control decisions in uncertain environments. Design-to-Criteria uses a heuristic approach for on-line scheduling of medium granularity tasks.It approximates the analysis used to generate an optimal policy by heuristically reasoning about the implications of uncertainty in task execution. The addition of uncertainty has also spawned a post-scheduling contingency analysis step for situations in which an agent must produce a result by a given deadline (deadline critical situations) and where the added computational cost is worth the expense. We describe the uncertainty representation in TÆMS and how it improves task models and the scheduling process, and provide empirical examples of reasoning about uncertainty in action. We also evaluate the performance of our heuristic-based approach to agent control using the performance of the policy generated by an optimal controller as the benchmark.
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References
Amant, R. St., Kuwata, Y., & Cohen, P. (1995). Monitoring progress with dynamic programming envelopes. In Proceedings of the seventh international IEEE conference on tools with artificial intelligence: (pp. 426–433).
Arnt A., Zilberstein S., Allan J. and Mouaddib A.I. (2004). Dynamic composition of information retrieval techniques. Journal of Intelligent Information Systems 23(1): 67–97
Barto A. G., Bradtke S. J. and Singh S. P. (1995). Learning to act using real-time dynamic programming. Artificial Intelligence 72: 81–138
Boutilier, C., Dean, T., & Hanks, S. (1995). Planning under uncertainty: Structural assumptions and computational leverage. In Proceedings of 3rd European workshop on planning (EWSP’95)
Bresina, J., Drummond, M., & Swanson, K. (1994). Just-in-case scheduling. In Proceedings of the twelfth national conference on artificial intelligence
Dean, T., & Boddy, M. (1988). An analysis of time-dependent planning. In Proceedings of the seventh national conference on artificial intelligence (pp. 49–54). St. Paul, Minnesota.
Dean T., Kaelbling L., Kirman J. and Nicholson A. (1995). Planning under time constraints in stochastic domains. Artificial Intelligence 76(1–2): 35–74
Dearden R. and Boutilier C. (1997). Abstraction and approximate decision-theoretic planning. Artificial Intelligence 89: 219–283
Decker, K. S. (1995). Environment centered analysis and design of coordination mechanisms. PhD thesis. University of Massachusetts.
Decker K. S. and Lesser V. R (1993). Quantitative modeling of complex environments. International Journal of Intelligent Systems in Accounting, Finance and Management 2(4): 215–234
Decker, K., & Li, J. (1998). Coordinated hospital patient scheduling. In Proceedings of the third international conference on multi-agent systems (ICMAS98) (pp. 104–111).
Draper, D., Hanks, S., & Weld, D. (1994). Probabilistic planning with information gathering and contingent execution. In Proceedings of the second international conference on artificial intelligence planning systems (AIPS-94) (pp. 31–36).
Garvey, A. (1996). Design-to-time real-time scheduling. PhD thesis. University of Massachusetts.
Garvey, A., Humphrey, M., & Lesser, V. (1993). Task interdependencies in design-to-time real-time scheduling. In Proceedings of the eleventh national conference on artificial intelligence (pp. 580–585). Washington, DC.
Garvey A. and Lesser V. (1993). Design-to-time real-time scheduling. IEEE Transactions on Systems, Man and Cybernetics 23(6): 1491–1502
Garvey, A., & Lesser, V. (1995). Representing and scheduling satisficing tasks. In Swaminathan Natarajan (Ed.), Imprecise and approximate computation (pp. 23–34). Norwell, MA: Kluwer Academic Publishers.
Haddaway P. and Hanks S. (1998). Utility models for goal-directed decision-theoretic planners. Computational Intelligence 14(3): 392–429
Horling, B., Benyo, B., & Lesser, V. (2001). Using self-diagnosis to adapt organizational structures. Proceedings of the 5th international conference on autonomous agents (pp. 529–536).
Horling B., Lesser V., Vincent R. and Wagner T. (2006). The soft real-time agent control architecture. Autonomous Agents and Multi-Agent Systems 12(1): 35–91
Horling, B., Lesser, V., Vincent, R., Wagner, T., Raja, A., Zhang, S., Decker, K., & Garvey, A. (1999). The TAEMS White Paper. Unpublished.
Horling, B., Vincent, R., Mailler, R., Shen, J., Becker, R., Rawlins, K., & Lesser, V. (2001). Distributed sensor network for real time tracking. Proceedings of the 5th international conference on autonomous agents (pp. 417–424).
Horvitz, E., Cooper, G., & Heckerman, D. (1989). Reflection and action under scarce resources: Theoretical principles and empirical study. In Proceedings of the eleventh international joint conference on artificial intelligence.
Horvitz, E., & Lengyel, J. (1996). Flexible rendering of 3D graphics under varying resources: Issues and directions. In Proceedings of the AAAI symposium on flexible computation in intelligent systems. Cambridge, Massachusetts.
‘Jensen, D., Atighetchi, M., Vincent, R., & Lesser, V. (1999). Learning quantitative knowledge for multiagent coordination. Under review, also available as UMASS Department of Computer Science Technical Report TR-99-04.
Kushmerick, N., Hanks, S., & Weld, D. (1994). An algorithm for probabilistic planning. In Proceedings of the twelfth national conference on artificial intelligence.
Lesser, V., Atighetchi, M., Benyo, B., Horling, B., Raja, A., Vincent, R., Wagner, T., Xuan, P., & Zhang, S. (1999). The UMASS Intelligent Home Project. In Proceedings of the third international conference on autonomous agents (pp. 291–298). Seattle.
Lesser V., Decker K., Wagner T., Carver N., Garvey A., Horling B., Neiman D., Podorozhny R., NagendraPrasad M., Raja A., Vincent R., Xuan P. and Zhang X. Q. (2004). Evolution of the gpgp taems domain-independent coordination framework. Autonomous Agents and Multi-Agent Systems 9(1): 87–143
Lesser V., Horling B., Klassner F., Raja A., Wagner T. and Zhang S. (2000). BIG: an agent for resource-bounded information gathering and decision making. Artificial Intelligence Journal, Special Issue on Internet Information Agents, 118(1–2): 197–244
Lesser V., Horling B., Raja A., Wagner T. and Zhang X. (2000). Resource-bounded searches in an information marketplace. IEEE Internet Computing: Agents on the Net 4(2): 49–57
Musliner, D. (1996). Plan execution in mission-critical domains. In Working notes of the AAAI fall symposium on plan execution –Problems and issues.
Onder, N., & Pollack, M. (1997). Contingency selection in plan generation. In Proceedings of the Fourth European Conference on Planning.
Raja, A., Lesser, V., & Wagner, T. (2000). Toward robust agent control in open environments.: In Proceedings of the fourth international conference on autonomous agents: (pp. 84–91). Barcelona, Catalonia, Spain: ACM Press.
Russell, S., & Zilberstein, S. (1991). Composing real-time systems. In Proceedings of the twelfth international joint conference on artificial intelligence (pp. 212–217). Sydney, Australia.
Simon H. (1945). Administrative behavior. Macmillan Company, New York, NY
Simon H. (1996). Models of bounded rationality. MIT Press, Cambridge, MA
Slany, W. (1996). Scheduling as a fuzzy multiple criteria optimization problem. Fuzzy Sets and Systems, 78, 197–222. Issue 2. Special Issue on Fuzzy Multiple Criteria Decision Making; URL: ftp://ftp.dbai. tuwien.ac.at/pub/papers/slany/fss96.ps.gz.
Tash, J., & Russell, S. (1994). Control strategies for a stochastic planner. In Proceedings of the eleventh national conference on artificial intelligence, (pp. 1079–1085).
Vincent, R., Horling, B., Lesser, V., & Wagner, T. (2001). Implementing soft real-time agent control. Proceedings of the 5th international conference on autonomous agents (pp. 355–362).
Wagner, T. (2000). Toward quantified, organizationally centered, decision making and coordination. PhD thesis. University of Massachusetts.
Wagner, T., Garvey, A., & Lesser, V. (1997). Complex goal criteria and its application in Design-to-Criteria Scheduling. In Proceedings of the fourteenth national conference on artificial intelligence (pp. 294–301). Also available as UMASS CS TR-1997-10.
Wagner, T., Garvey, A., & Lesser, V. (1998). Criteria-directed heuristic task scheduling. International Journal of Approximate Reasoning, Special Issue on Scheduling, 19(1–2), 91–118, A version also available as UMASS CS TR-97-59.
Wagner, T., Guralnik, V., & Phelps, J. (2003). A key-based coordination algorithm for dynamic readiness and repair service coordination. Proceedings of the 2nd international conference on autonomous agents and MAS, (AAMAS2003) (pp. 1140–1147).
Wagner T., Guralnik V. and Phelps J. (2003). Software agents: Enabling dynamic supply chain management for a build to order product line. International Journal of Electronic Commerce Research and Applications, Special Issue on Software Agents for Business Automation, 2: 114–132
Wagner, T., Horling, B., Lesser, V., Phelps, J., & Guralnik, V. (2003). The struggle for reuse: Pros and cons of generalization in taems and its impact on technology transition. Proceedings of the ISCA 12th International conference on intelligent and adaptive systems and software engineering (IASSE-2003).
Wagner, T., & Lesser, V. (2000). Design-to-Criteria scheduling: real-time agent control. Proceedings of AAAI 2000 spring symposium on real-time autonomous systems (pp. 89–96).
Wagner T. and Lesser V. (2002). Evolving real-time local agent control for large-scale multi-agent systems. Intelligent Agents VIII: Agent Theories, Architectures and Languages, 2333: 51–68
Xuan P. and Lesser V. (2000). Incorporating uncertainty in agent commitments. Intelligent Agents VI: Agent Theories, Architectures and Languages 1757: 57–70
Zhang, X., Lesser, V., & Wagner, T. (2003). A two-level negotiation framework for complex negotiations. In Proceedings of the 2003 IEEE/WIC international conference on intelligent agent technology (IAT 2003) (pp. 311–317). Halifax, Canada: IEEE Computer Society.
Zilberstein S. (1996). Using anytime algorithms in intelligent systems. AI Magazine 17(3): 73–83
Zilberstein, S., & Russell, S. (1992). Constructing utility-driven real-time systems using anytime algorithms. In Proceedings of the IEEE workshop on imprecise and approximate computation (pp. 6–10). Phoenix, AZ.
Zilberstein S. and Russell S. (1996). Optimal composition of real-time systems. Artificial Intelligence 82(1): 181–214
Zweben M., Daun B., Davis E., & Deale M. (1994). Scheduling and rescheduling with iterative repair. In M. Zweben, & M. Fox (Eds.), Intelligent scheduling (chapter 8). Morgan Kaufmann.
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An erratum to this article is available at http://dx.doi.org/10.1007/s10458-006-9959-0.
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Wagner, T.A., Raja, A. & Lesser, V.R. Modeling Uncertainty and its Implications to Sophisticated Control in Tæms Agents. Auton Agent Multi-Agent Syst 13, 235–292 (2006). https://doi.org/10.1007/s10458-006-7669-2
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DOI: https://doi.org/10.1007/s10458-006-7669-2