Cooperative Epistemic Multi-Agent Planning for Implicit Coordination

Engesser, Thorsten; Bolander, Thomas; Mattmüller, Robert; Nebel, Bernhard

doi:10.4204/eptcs.243.6

Cited by 45 publications

(49 citation statements)

References 21 publications

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“…Going one step further, suppose we do not know anything like Proposition 32 from [13] that allows us to restrict the search space. This perspective was also adopted in [23], turning the puzzle into an epistemic planning problem. If we only fix that Alice will announce five hands, including her own which w.l.o.g.…”

Section: Russian Cardsmentioning

confidence: 99%

Symbolic model checking for Dynamic Epistemic Logic — S5 and beyond*

Benthem

Eijck

Gattinger

et al. 2017

Journal of Logic and Computation

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Dynamic Epistemic Logic (DEL) can model complex information scenarios in a way that appeals to logicians. However, existing DEL implementations are ad-hoc, so we do not know how the framework really performs. For this purpose, we want to hook up with the best available model-checking and SAT techniques in computational logic. We do this by first providing a bridge: a new faithful representation of DEL models as so-called knowledge structures that allow for symbolic model checking. For more complex epistemic change we introduce knowledge transformers analogous to action models. Next, we show that we can now solve well-known benchmark problems in epistemic scenarios much faster than with existing methods for DEL. We also compare our approach to model checking for temporal logics. Finally, we show that our method is not just a matter of implementation, but that it raises significant issues about logical representation and update. 7

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Section: Russian Cardsmentioning

confidence: 99%

Symbolic model checking for Dynamic Epistemic Logic — S5 and beyond*

Benthem

Eijck

Gattinger

et al. 2017

Journal of Logic and Computation

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“…While there exist solutions aimed at solving situations that require cooperative planning processes [7,18,10], they usually consider joint goals situations, either relying on central coordination systems or being limited by the problem domain. AA could benefit from cooperative planning in situations where individual efficiency is affected by environmental conditions, ultimately executing certain tasks more efficiently.…”

Section: Planning and Cooperationmentioning

confidence: 99%

“…While extensive research has been conducted in the field of intelligent agent cooperative planning [7,18,10,13], most of the existing work relies on central coordination or communication mechanisms. Such restrictions can make it difficult to use the existing research in AmI scenarios where agents possess individual goals, but cooperation without previous commitment is still beneficial.…”

Section: Introductionmentioning

confidence: 99%

Fostering Agent Cooperation in AmI: A Context-Aware Mechanism for Dealing with Multiple Intentions

Casals¹,

Belbachir²,

Seghrouchni³

et al. 2018

Intelligent Distributed Computing XII

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Ambient Intelligent (AmI) environments dynamically provide contextual information to intelligent agents that interact with them. In such environments, could these agents cooperate to improve their goal achievement, considering multiple intentions from several agents? With multiple agents, cooperation will depend on each agent's own intentions. Agents adapt to dynamic changes in the environment using context-aware planning mechanisms such as the Contextual Planning System (CPS), which proposes an optimal plan for a single agent based on the current context. In this paper we present the Collective CPS (CCPS), an opportunistic cooperative planning mechanism for multiple agents in AmI environments. CCPS allows agents to partially delegate their own plans or to collaborate with other agents' plans during their execution, while retaining individual planning capabilities. A working scenario is shown for a realistic AmI environment, such as a smart Campus.

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“…Several authors have investigated how Propositional Dynamic Logic PDL can account for conformant planning [2,5,11,12]. We here push this program further and investigate how contingent planning can be captured in PDL.…”

Section: Introductionmentioning

confidence: 99%

“…Things are less straightforward when actions can be nondeterministic. We follow [1,2,5,12] and introduce a third modal operator ([a 1 ; . .…”

Section: Introductionmentioning

confidence: 99%

The Dynamic Logic of Policies and Contingent Planning

Bolander

Engesser

Herzig

et al. 2019

Logics in Artificial Intelligence

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In classical deterministic planning, solutions to planning tasks are simply sequences of actions, but that is not sufficient for contingent plans in non-deterministic environments. Contingent plans are often expressed through policies that map states to actions. An alternative is to specify contingent plans as programs, e.g. in the syntax of Propositional Dynamic Logic (PDL). PDL is a logic for reasoning about programs with sequential composition, test and non-deterministic choice. However, as we show in the paper, none of the existing PDL modalities directly captures the notion of a solution to a planning task under non-determinism. We add a new modality to star-free PDL correctly capturing this notion. We prove the appropriateness of the new modality by showing how to translate back and forth between policies and PDL programs under the new modality. More precisely, we show how a policy solution to a planning task gives rise to a program solution expressed via the new modality, and vice versa. We also provide an axiomatisation of our PDL extension through reduction axioms into standard star-free PDL.

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Cooperative Epistemic Multi-Agent Planning for Implicit Coordination

Cited by 45 publications

References 21 publications

Symbolic model checking for Dynamic Epistemic Logic — S5 and beyond*

Symbolic model checking for Dynamic Epistemic Logic — S5 and beyond*

Fostering Agent Cooperation in AmI: A Context-Aware Mechanism for Dealing with Multiple Intentions

The Dynamic Logic of Policies and Contingent Planning

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