Planning and reacting in uncertain and dynamic environments

Abstract Agents situated in dynamic and uncertain environments require several capabilities for successful operation. Such agents must monitor the world and respond appropriately to important events. The agents should be able to accept goals, synthesize complex plans for achieving those goals, and execute the plans while continuing to be responsive to changes in the world. As events render some current activities obsolete, the agents should be able to modify their plans while continuing activities unaffected by those events. The Cypress system is a domain-independent framework for defining persistent agents with this full range of behaviour. Cypress has been used for several demanding applications, including military operations, real-time tracking, and fault diagnosis.

[1]  David E. Wilkins,et al.  Practical planning - extending the classical AI planning paradigm , 1989, Morgan Kaufmann series in representation and reasoning.

[2]  James A. Hendler,et al.  A Validation-Structure-Based Theory of Plan Modification and Reuse , 1992, Artif. Intell..

[3]  Bernhard Nebel,et al.  Plan Modification versus Plan Generation: A Complexity-Theoretic Perspective , 1993, IJCAI.

[4]  R. James Firby,et al.  An Investigation into Reactive Planning in Complex Domains , 1987, AAAI.

[5]  Austin Tate,et al.  O-Plan: The open Planning Architecture , 1991, Artif. Intell..

[6]  Steven A. Vere,et al.  Planning in Time: Windows and Durations for Activities and Goals , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  D. McDermott Transformational Planning of Reactive Behavior , 1992 .

[8]  Eric Horvitz,et al.  Reflection and Action Under Scarce Resources: Theoretical Principles and Empirical Study , 1989, IJCAI.

[9]  Frank Maurer,et al.  Integrating planning and execution in software development processes , 1996, Proceedings of WET ICE '96. IEEE 5th Workshop on Enabling Technologies; Infrastucture for Collaborative Enterprises.

[10]  David E. Wilkins,et al.  Can AI planners solve practical problems? , 1990, Comput. Intell..

[11]  John D. Lowrance,et al.  Explaining evidential analyses , 1989, Int. J. Approx. Reason..

[12]  Paul R. Cohen,et al.  Failure Recovery: A Model and Experiments , 1991, AAAI.

[13]  John D. Lowrance,et al.  A Framework for Evidential-Reasoning Systems , 1990, AAAI.

[14]  J. Lowrance,et al.  Plan Evaluation under Uncertainty , 1990 .

[15]  Richard Washington,et al.  Abstraction planning in real time , 1994 .

[16]  Mark S. Boddy,et al.  Solving Time-Dependent Planning Problems , 1989, IJCAI.

[17]  David E. Wilkins,et al.  Applying an AI Planner to Military Operations Planning , 1993 .

[18]  M. Brady,et al.  Recognizing Intentions From Natural Language Utterances , 1983 .

[19]  Michael P. Georgeff,et al.  Decision-Making in an Embedded Reasoning System , 1989, IJCAI.

[20]  Leslie Pack Kaelbling,et al.  An Architecture for Intelligent Reactive Systems , 1987 .

[21]  Barbara Hayes-Roth,et al.  Practical Real-Time Planning , 1992 .

[22]  Marcel Schoppers,et al.  Universal Plans for Reactive Robots in Unpredictable Environments , 1987, IJCAI.

[23]  James F. Allen Maintaining knowledge about temporal intervals , 1983, CACM.

[24]  David J. Israel,et al.  Plans and resource‐bounded practical reasoning , 1988, Comput. Intell..

[25]  David E. Wilkins,et al.  A Common Knowledge Representation for Plan Generation and Reactive Execution , 1995, J. Log. Comput..

[26]  Damian M. Lyons,et al.  A practical approach to integrating reaction and deliberation , 1992 .

[27]  Thomas M. Strat,et al.  Decision analysis using belief functions , 1990, Int. J. Approx. Reason..

[28]  Erann Gat,et al.  Integrating Planning and Reacting in a Heterogeneous Asynchronous Architecture for Controlling Real-World Mobile Robots , 1992, AAAI.

[29]  Earl David Sacerdoti,et al.  A Structure for Plans and Behavior , 1977 .