On-Line Decision-Theoretic Golog for Unpredictable Domains

DTGolog was proposed by Boutilier et al. as an integration of decision-theoretic (DT) planning and the programming language Golog. Advantages include the ability to handle large state spaces and to limit the search space during planning with explicit programming. Soutchanski developed a version of DTGolog, where a program is executed on-line and DT planning can be applied to parts of a program only. One of the limitations is that DT planning generally cannot be applied to programs containing sensing actions. In order to deal with robotic scenarios in unpredictable domains, where certain kinds of sensing like measuring one’s own position are ubiquitous, we propose a strategy where sensing during deliberation is replaced by suitable models like computed trajectories so that DT planning remains applicable. In the paper we discuss the necessary changes to DTGolog entailed by this strategy and an application of our approach in the RoboCup domain.

[1]  Michael Beetz,et al.  The AGILO Robot Soccer Team—Experience-Based Learning and Probabilistic Reasoning in Autonomous Robot Control , 2004, Auton. Robots.

[2]  Hector J. Levesque,et al.  GOLOG: A Logic Programming Language for Dynamic Domains , 1997, J. Log. Program..

[3]  Murray Shanahan,et al.  The Event Calculus Explained , 1999, Artificial Intelligence Today.

[4]  David Poole,et al.  The Independent Choice Logic for Modelling Multiple Agents Under Uncertainty , 1997, Artif. Intell..

[5]  Yves Lesprance,et al.  Integrating Planning into Reactive High-Level Robot Programs , 2000 .

[6]  Raymond Reiter,et al.  Logical Foundations for Cognitive Agents: Contributions in Honor of Ray Reiter , 2001 .

[7]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[8]  Axel Großmann,et al.  Symbolic Dynamic Programming within the Fluent Calculus , 2002 .

[9]  Gerhard Lakemeyer,et al.  On-Line Execution of cc-Golog Plans , 2001, IJCAI.

[10]  Michael Wooldridge,et al.  Artificial Intelligence Today , 1999, Lecture Notes in Computer Science.

[11]  Craig Boutilier,et al.  Decision-Theoretic, High-Level Agent Programming in the Situation Calculus , 2000, AAAI/IAAI.

[12]  Craig Boutilier,et al.  Symbolic Dynamic Programming for First-Order MDPs , 2001, IJCAI.

[13]  John G. Gibbons Knowledge in Action , 2001 .

[14]  Hector J. Levesque,et al.  An Incremental Interpreter for High-Level Programs with Sensing , 1999 .

[15]  Fangzhen Lin,et al.  How to Progress a Database , 1997, Artif. Intell..

[16]  Tonya Lewis,et al.  Knowledge in Action , 1977 .

[17]  J. McCarthy Situations, Actions, and Causal Laws , 1963 .

[18]  Mikhail Soutchanski,et al.  An On-line Decision-Theoretic Golog Interpreter , 2001, IJCAI.

[19]  Hector J. Levesque,et al.  ConGolog, a concurrent programming language based on the situation calculus , 2000, Artif. Intell..