Intention Progression with Temporally Extended Goals
Intention Progression with Temporally Extended Goals
Yuan Yao, Natasha Alechina, Brian Logan
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 292-301.
https://doi.org/10.24963/ijcai.2024/33
The Belief-Desire-Intention (BDI) approach to agent development has formed the basis for much of the research on architectures for autonomous agents. A key advantage of the BDI approach is that agents may pursue multiple intentions in parallel. However, previous approaches to managing possible interactions between concurrently executing intentions are limited to interactions between simple achievement goals (and in some cases maintenance goals). In this paper we present a new approach to intention progression for agents with temporally extended goals which allow mixing reachability and invariant properties, e.g., ``travel to location A while not exceeding a gradient of 5%''. Temporally extended goals may be specified at run-time (top-level goals), and as subgoals in plans. In addition, our approach allows human-authored plans and plans implemented as RL policies to be freely mixed in an agent program, allowing the development of agents with `neuro-symbolic' architectures.
Keywords:
Agent-based and Multi-agent Systems: MAS: Agent theories and models
Agent-based and Multi-agent Systems: MAS: Engineering methods, platforms, languages and tools