A Formal Approach to Embedding First-Principles Planning in BDI Agent Systems

Abstract

The BDI architecture, where agents are modelled based on their beliefs, desires, and intentions, provides a practical approach to developing intelligent agent systems. However, these systems either do not include any capability for first-principles planning (FPP), or they integrate FPP in a rigid and ad-hoc manner that does not define the semantical behaviour. In this paper, we propose a novel operational semantics for incorporating FPP as an intrinsic planning capability to achieve goals in BDI agent systems. To achieve this, we introduce a declarative goal intention to keep track of declarative goals used by FPP and develop a detailed specification of the appropriate operational behaviour when FPP is pursued, succeeded or failed, suspended, or resumed in the BDI agent systems. Furthermore, we prove that BDI agent systems and FPP are theoretically compatible for principled integration in both offline and online planning manner. The practical feasibility of this integration is demonstrated, and we show that the resulting agent framework combines the strengths of both BDI agent systems and FPP, thus substantially improving the performance of BDI agent systems when facing unforeseen situations.

Probabilistic BDI Agents: Actions, Plans, and Intentions

“…There is a large amount of work (surveyed in [39]) to employ planning to dynamically synthesise new plans to achieve an event even when no pre-defined plan worked or exists. One work [40], for example, shows in detail how the integration of planning and BDI can be done at the semantic level.…”

SMASH: a Semantic-enabled Multi-agent Approach for Self-adaptation of Human-centered IoT

“…Deliberative planning is defined as a way to seek the achievement of goals by executing plans, through the realization of tasks, reaching a contextual state, or the maintenance of a condition. In [17,18,19,20,21], the challenges of merging planning and acting are addressed. Besides, some approaches are presenting goal-reasoning architectures [22,23] that are practical for adaptation of intelligent agents, which may control IoT objects of the smart environment.…”

“…The used planning language contains domain actions that matches to the know-what of the proposed agent architecture, and the problem initial state is built upon the set of beliefs present in our agent. Furthermore, there are works illustrating how flexible the planning and acting functions are, covering integrations with hierarchical operational models [17], with hierarchical task networks (HTN) [19], with first-principles planning (FPP) [20], with the belief-desire-intention (BDI) agent model [21], and with other deliberation functions such as monitoring, observing, and learning [18].…”