Abstract
A key problem for Belief-Desire-Intention (BDI) agents is intention
progression, i.e., which plans should be selected and how the execution of these plans should be interleaved so as to achieve the
agent’s goals. Monte-Carlo Tree Search (MCTS) has been shown
to be a promising approach to the intention progression problem,
out-performing other approaches in the literature. However, MCTS
relies on runtime simulation of possible interleavings of the plans
in each intention, which may be computationally costly. In this paper, we introduce the notion of quantitative summary information
which can be used to estimate the likelihood of conflicts between
an agent’s intentions. We show how offline simulation can be used
to precompute quantitative summary information prior to execution of the agent’s program, and how the precomputed summary
information can be used at runtime to guide the expansion of the
MCTS search tree and avoid unnecessary runtime simulation. We
compare the performance of our approach with standard MCTS
in a range of scenarios of increasing difficulty. The results suggest
our approach can significantly improve the efficiency of MCTS in
terms of the number of runtime simulations performed.
progression, i.e., which plans should be selected and how the execution of these plans should be interleaved so as to achieve the
agent’s goals. Monte-Carlo Tree Search (MCTS) has been shown
to be a promising approach to the intention progression problem,
out-performing other approaches in the literature. However, MCTS
relies on runtime simulation of possible interleavings of the plans
in each intention, which may be computationally costly. In this paper, we introduce the notion of quantitative summary information
which can be used to estimate the likelihood of conflicts between
an agent’s intentions. We show how offline simulation can be used
to precompute quantitative summary information prior to execution of the agent’s program, and how the precomputed summary
information can be used at runtime to guide the expansion of the
MCTS search tree and avoid unnecessary runtime simulation. We
compare the performance of our approach with standard MCTS
in a range of scenarios of increasing difficulty. The results suggest
our approach can significantly improve the efficiency of MCTS in
terms of the number of runtime simulations performed.
| Original language | English |
|---|---|
| Title of host publication | 20th International Conference on Autonomous Agents and Multiagent Systems, 3/05/21 |
| Publication status | Published - 2021 |
| Externally published | Yes |