Comparing Variable Handling Strategies in BDI Agents: Experimental Study

Comparing Variable Handling Strategies in BDI Agents: Experimental Study

Paper in proceedings (conference paper)

BDI (Belief-Desire-Intention) agents represent a paradigm in artificial intelligence, demonstrating proficiency in reasoning, planning, and decision-making. They offer a versatile framework to construct intelligent agents capable of reasoning about their beliefs, desires, and intentions. Our research focuses on AgentSpeak(L), a popular BDI language, and its interpreter using late variable bindings. Unlike traditional interpreters, it defers substitution selection until execution, enhancing rationality by preventing premature, erroneous selections. To validate our approach, we conducted experiments in a virtual collectable card marketplace. We implemented a system that can use both late and early variable binding strategies, comparing their performance. In shared and independent experiments, the late bindings strategy outperformed the early bindings strategy, although overhead costs were observed. We also conduct a brief discussion of the situations in which it is appropriate to use late bindings given the structure of the declared plans.

BDI (Belief-Desire-Intention) agents represent a paradigm in artificial intelligence, demonstrating proficiency in reasoning, planning, and decision-making. They offer a versatile framework to construct intelligent agents capable of reasoning about their beliefs, desires, and intentions. Our research focuses on AgentSpeak(L), a popular BDI language, and its interpreter using late variable bindings. Unlike traditional interpreters, it defers substitution selection until execution, enhancing rationality by preventing premature, erroneous selections. To validate our approach, we conducted experiments in a virtual collectable card marketplace. We implemented a system that can use both late and early variable binding strategies, comparing their performance. In shared and independent experiments, the late bindings strategy outperformed the early bindings strategy, although overhead costs were observed. We also conduct a brief discussion of the situations in which it is appropriate to use late bindings given the structure of the declared plans.

BDI Agents, Agent Interpretation, AgentSpeak(L)

BDI Agents, Agent Interpretation, AgentSpeak(L)

VÍDEŇSKÝ, F.; ZBOŘIL, F.; BERAN, J.; KOČÍ, R.; ZBOŘIL, F.

2025

04.02.2024

SciTePress - Science and Technology Publications

Rome

978-989-758-680-4

Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1

25

36

12

https://www.fit.vut.cz/research/publication/13085/

http://hdl.handle.net/11012/254263

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