Exploring Heuristic Action Selection in Agent Programming

Rational agents programmed in agent programming lan- guages derive their choice of action from their beliefs and goals. One of the main benefits of such programming languages is that they facilitate a high-level and conceptually elegant specification of agent behaviour. Qualitative concepts alone, however, are not sufficient to specify that this behaviour is also nearly optimal, a quality typically also associated with rational agents. Optimality in this context refers to the costs and rewards associated with action execution. It thus would be useful to extend agent programming languages with primitives that allow the specification of near-optimal behaviour. The idea is that quantitative heuristics added to an agent program prune some of the options generated by the qualitative action selection mechanism. In this paper, we explore the expressivity needed to specify such behaviour in the Blocks World domain. The programming constructs that we introduce allow for a high-level specification of such heuristics due to the fact that these can be defined by (re)using the qualitative notions of the basic agent programming language again. We illustrate the use of these constructs by extending a Goal Blocks World agent with various strategies to optimize its behaviour.

[1]  Stephen A. Cook,et al.  A Complete Axiomatization for Blocks World , 2002, J. Log. Comput..

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

[3]  Anand S. Rao,et al.  An architecture for real-time reasoning and system control , 1992, IEEE Expert.

[4]  Walter Van de Velde,et al.  Agents Breaking Away , 1996, Lecture Notes in Computer Science.

[5]  John K. Slaney,et al.  Blocks World revisited , 2001, Artif. Intell..

[6]  Victor R. Lesser,et al.  AgentSpeak(XL): efficient intention selection in BDI agents via decision-theoretic task scheduling , 2002, AAMAS '02.

[7]  Koen V. Hindriks,et al.  Modules as Policy-Based Intentions: Modular Agent Programming in GOAL , 2007, PROMAS.

[8]  Craig Boutilier,et al.  Decision-Theoretic Planning: Structural Assumptions and Computational Leverage , 1999, J. Artif. Intell. Res..

[9]  Fahiem Bacchus,et al.  Using temporal logics to express search control knowledge for planning , 2000, Artif. Intell..

[10]  A. S. Roa,et al.  AgentSpeak(L): BDI agents speak out in a logical computable language , 1996 .

[11]  Jesus A. Gonzalez,et al.  Advances in Artificial Intelligence – IBERAMIA 2004 , 2004, Lecture Notes in Computer Science.

[12]  Dana S. Nau,et al.  On the Complexity of Blocks-World Planning , 1992, Artif. Intell..

[13]  Koen V. Hindriks,et al.  A verification framework for agent programming with declarative goals , 2007, J. Appl. Log..

[14]  Hector J. Levesque,et al.  Intention is Choice with Commitment , 1990, Artif. Intell..

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