BDI Logics for BDI Architectures: Old Problems, New Perspectives

Herzig, Andreas; Lorini, Emiliano; Perrussel, Laurent; Xiao, Zhou

doi:10.1007/s13218-016-0457-5

Cited by 36 publications

(26 citation statements)

References 51 publications

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“…Previous work embedding HTN in the Situation Calculus [1,6,7] is discussed in more detail in the long report. Relations between HTN planning with the semantics of BDI logics are investigated in [3,9,10,14].…”

Section: Resultsmentioning

confidence: 99%

On Hierarchical Task Networks

Herzig

Perrussel

Xiao

2016

Logics in Artificial Intelligence

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. Abstract. In planning based on hierarchical task networks (HTN), plans are generated by refining high-level actions ('compound tasks') into lower-level actions, until primitive actions are obtained that can be sent to execution. While a primitive action is defined by its precondition and effects, a high-level action is defined by zero, one or several methods: sets of (high-level or primitive) actions decomposing it together with a constraint. We give a semantics of HTNs in terms of dynamic logic with program inclusion. We propose postulates guaranteeing soundness and completeness of action refinement. We also show that hybrid planning can be analysed in the same dynamic logic framework.

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Section: Resultsmentioning

confidence: 99%

On Hierarchical Task Networks

Herzig

Perrussel

Xiao

2016

Logics in Artificial Intelligence

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“…Regarding the requirements proposed by Herzig et al [23] for a formal theory of agent programming, we believe our work tackles most of the problems identified by those authors. It remains, however, to provide a greater connection of our logics with the work areas as planning and game theory.…”

Section: Final Considerationsmentioning

confidence: 86%

“…Recently, Herzig et al [23] pointed out some deficiencies in the formal frameworks for specifying BDI agents which are available in the literature. The authors point out the advantages of a formal theory with a close relationship with the work in belief dynamics and with agent programming.…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Preference Logic for Reasoning about Agent Programming

Souza

Moreira

Vieira

et al. 2017

2017 Brazilian Conference on Intelligent Systems (BRACIS)

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In this work, we investigate the use of Dynamic Preference Logic to encode BDI mental attitudes. Further, exploring this codification and the representation of preferences over possible worlds by preferences over propositional formulas, here called priority graphs, we comment on how to interpret BDI agent programs in this logic. Also, using the connection between dynamic operations defined over preference models and their encoding as transformations on priority graphs, we show how our logic can be used not only to reason about agent programs, but as a tool to specify reasoning mechanisms to guarantee certain properties in the theory of rationality for the programming language.

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“…Each plan describes how an agent can react to an (external/internal) event under specific conditions, and the set of intentions are those plans that are currently being executed. Both beliefs and plan libraries are well-studied, but intentions -a crucial part of BDI agents -are one of the least studied areas in BDI theory [7].…”

Section: Introductionmentioning

confidence: 99%

Intention Interleaving Via Classical Replanning

McAreavey

Bauters

et al. 2019

2019 IEEE 31st International Conference on Tools With Artificial Intelligence (ICTAI)

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The BDI architecture, where agents are modelled based on their belief, desires, and intentions, provides a practical approach to developing intelligent agents. One of the key features of BDI agents is that they are able to pursue multiple intentions in parallel, i.e. in an interleaved manner. Most of the previous works have enabled BDI agents to avoid negative interactions between intentions to ensure the correct execution. However, to avoid execution inefficiencies, BDI agents should also capitalise on positive interactions between intentions. In this paper, we provide a theoretical framework where first-principles planning (FPP) is employed to manage the intention interleaving in an automated fashion. Our FPP approach not only guarantees the achievability of intentions, but also discovers and exploits potential common sub-intentions to reduce the overall cost of intention execution. Our results show that our approach is both theoretically sound and practically feasible. The effectiveness evaluation in a manufacturing scenario shows that our approach can significantly reduce the total number of actions by merging common sub-intentions, while still accomplishing all intentions.

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BDI Logics for BDI Architectures: Old Problems, New Perspectives

Cited by 36 publications

References 51 publications

On Hierarchical Task Networks

On Hierarchical Task Networks

A Dynamic Preference Logic for Reasoning about Agent Programming

Intention Interleaving Via Classical Replanning

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