Integrating Late Variable Binding with SP-MCTS for Efficient Plan Execution in BDI Agents

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.