Publication Details
Integrating Late Variable Binding with SP-MCTS for Efficient Plan Execution in BDI Agents
Zbořil František, doc. Ing., Ph.D. (DITS)
Veigend Petr, Ing., Ph.D. (DITS)
BDI Agents, Agent Interpretation, AgentSpeak(L), Monte Carlo Tree Search
This paper investigates the Late binding strategy as an enhancement to the
SP-MCTS algorithm for intention selection and variable binding in BDI
(Belief-Desire-Intention) agents. Unlike the Early binding strategy, which
selects variable substitutions prematurely, Late binding defers these decisions
until necessary, aggregating all substitutions for a plan into a single node.
This approach reduces the search tree size and enhances adaptability in dynamic
environments by maintaining flexibility during plan execution. We implemented the
Late binding strategy within the FRAg system to validate our approach and
conducted experiments in a static maze task environment. Experimental results
demonstrate that the Late binding strategy consistently outperforms Early
binding, achieving up to 150\% higher rewards, particularly for the lowest
parameter values of the SP-MCTS algorithm in resource-constrained scenarios.
These results confirm that it is feasible to integrate Late binding into
intention selection methods, opening opportunities to explore its use in
approaches with lower computational demands than the SP-MCTS algorithm.
@inproceedings{BUT193742,
author="František {Vídeňský} and František {Zbořil} and Petr {Veigend}",
title="Integrating Late Variable Binding with SP-MCTS for Efficient Plan Execution in BDI Agents",
year="2025"
}