Toward agent programs with circuit semantics

New ideas are presented for computing and organizing actions for autonomous agents in dynamic environments - environments in which the agent''s current situation cannot always be accurately discerned and in which the effects of actions cannot always be reliably predicted. The notion of "circuit semantics" for programs based on "teleo-reactive trees" is introduced. Program execution builds a combinational circuit which receives sensory inputs and controls actions. These formalisms embody a high degree of inherent conditionality and thus yield programs that are suitably reactive to their environments. At the same time, the actions computed by the programs are guided by the overall goals of the agent. The paper also speculates about how programs using these ideas could be automatically generated by artificial intelligence planning systems and adapted by learning methods.

[1]  Richard Fikes,et al.  Learning and Executing Generalized Robot Plans , 1993, Artif. Intell..

[2]  Steven A. Vere,et al.  Inductive learning of relational productions , 1977, SGAR.

[3]  Frederick Hayes-Roth,et al.  AN OVERVIEW OF PATTERN-DIRECTED INFERENCE SYSTEMS , 1978 .

[4]  Nils J. Nilsson,et al.  Principles of Artificial Intelligence , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Wolfgang Reisig,et al.  Petri Nets , 1985, EATCS Monographs on Theoretical Computer Science.

[6]  Johan de Kleer,et al.  Problem Solving with the ATMS , 1986, Artif. Intell..

[7]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

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

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

[10]  Amy L. Lansky,et al.  Reactive Reasoning and Planning , 1987, AAAI.

[11]  Philip E. Agre,et al.  The dynamic structure of everyday life , 1988 .

[12]  Leslie Pack Kaelbling,et al.  Goals as Parallel Program Specifications , 1988, AAAI.

[13]  David Harel,et al.  On visual formalisms , 1988, CACM.

[14]  Wei-Min Shen,et al.  Learning from the environment based on percepts and actions , 1989 .

[15]  Mark Drummond,et al.  Situated Control Rules , 1989, KR.

[16]  Matthew L. Ginsberg,et al.  Universal Planning: An (Almost) Universally Bad Idea , 1989, AI Mag..

[17]  Amir Pnueli,et al.  On the Synthesis of an Asynchronous Reactive Module , 1989, ICALP.

[18]  John E. Laird,et al.  Integrating, Execution, Planning, and Learning in Soar for External Environments , 1990, AAAI.

[19]  Damian M. Lyons,et al.  A formal model for reactive robot plans , 1990, [1990] Proceedings. Rensselaer's Second International Conference on Computer Integrated Manufacturing.

[20]  Leslie Pack Kaelbling,et al.  Action and planning in embedded agents , 1990, Robotics Auton. Syst..

[21]  Tom M. Mitchell,et al.  Becoming Increasingly Reactive , 1990, AAAI.

[22]  David Chapman,et al.  Vision, instruction, and action , 1990 .

[23]  Rodney A. Brooks,et al.  The Behavior Language: User''s Guide , 1990 .

[24]  Alan D. Christiansen,et al.  Learning reliable manipulation strategies without initial physical models , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[25]  Richard S. Sutton,et al.  Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming , 1990, ML.

[26]  Long Ji Lin,et al.  Programming Robots Using Reinforcement Learning and Teaching , 1991, AAAI.

[27]  Erann Gat.  ALFA: a language for programming reactive robotic control systems , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[28]  Michael P. Wellman,et al.  Planning and Control , 1991 .

[29]  Jana Kosecka,et al.  Control of Discrete Event Systems , 1992 .

[30]  Sridhar Mahadevan,et al.  Automatic Programming of Behavior-Based Robots Using Reinforcement Learning , 1991, Artif. Intell..