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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0193742" target="_blank" >RIV/00216305:26230/26:0193742 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.fit.vut.cz/research/publication/13326/" target="_blank" >https://www.fit.vut.cz/research/publication/13326/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0013373900003890" target="_blank" >10.5220/0013373900003890</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART

ISBN

978-989-758-737-5

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

679-686

Název nakladatele

SciTePress - Science and Technology Publications

Místo vydání

Porto

Místo konání akce

Porto

Datum konání akce

23. 2. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—