Reasoning and Planning with Cooperative Actions for Multiagents Using Answer Set Programming

Son, Tran Cao; Sakama, Chiaki

doi:10.1007/978-3-642-11355-0_13

Cited by 11 publications

(7 citation statements)

References 18 publications

(26 reference statements)

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“…Early attempts to adapt action languages to formalize multi-agent domains can be found in [42,43,44]. In these works, the action languages A, B, and C have been extended to formalize multi-agent domains.…”

Section: Previous Work By the Authorsmentioning

confidence: 99%

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An Action Language for Multi-Agent Domains: Foundations

Baral,

Gelfond,

Pontelli

et al. 2015

Preprint

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In multi-agent domains, an agent's action may not just change the world and the agent's knowledge and beliefs about the world, but also may change other agents' knowledge and beliefs about the world and their knowledge and beliefs about other agents' knowledge and beliefs about the world. Similarly, the goals of an agent in a multi-agent world may involve manipulating the knowledge and beliefs of other agents' and again, not just their knowledge 1 about the world, but also their knowledge about other agents' knowledge about the world. The goal of this paper is to present an action language, called mA+, that has the necessary features to address the above aspects in representing and reasoning about actions and change in multi-agent domains.This action language can be viewed as a generalization of the single-agent action languages extensively studied in the literature, to the case of multi-agent domains. The language allows the representation of and reasoning about different types of actions that an agent can perform in a domain where many other agents might be present-such as world-altering actions, sensing actions, and announcement/communication actions. The action language also allows the specification of agents' dynamic awareness of action occurrences which has future implications on what agents' know about the world and other agents' knowledge about the world. The language mA+ considers three different types of awareness: full awareness, partial awareness, and complete oblivion of an action occurrence and its effects. This keeps the language simple, yet powerful enough to address a large variety of knowledge manipulation scenarios in multi-agent domains.The semantics of the language relies on the notion of state, which is described by a pointed Kripke model and is used to encode the agent's knowledge and the real state of the world. The semantics is defined by a transition function that maps pairs of actions and states into sets of states. The paper illustrates properties of the action theories, including properties that guarantee finiteness of the set of initial states and their practical implementability. Finally, the paper relates mA+ to other related formalisms that contribute to reasoning about actions in multi-agent domains.

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Section: Previous Work By the Authorsmentioning

confidence: 99%

“…The works in [43,44] investigate the use of action language in multi-agent planning context and focus on the generation of decentralized plans for multiple agents, to either jointly achieve a goal or individual goals.…”

Section: Previous Work By the Authorsmentioning

confidence: 99%

An Action Language for Multi-Agent Domains: Foundations

Baral,

Gelfond,

Pontelli

et al. 2015

Preprint

Self Cite

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“…A recent effort, along similar lines as ours, for modeling an action language for multi-agent systems, has been proposed by Son and Sakama [32], relying on the use of answer set programming (with consistency restoring rules) to address forms of synchronous communication among agents. The approach adopted in [32] is similar in spirit-it also proposes a high level action language which allows the description of multiple agents, by describing their individual capabilities. The language is quite different from B MAP ; in particular…”

Section: Related Workmentioning

confidence: 99%

“…• The language of [32] supports only Boolean fluents and it does not provide the declarative support of a constraint-based language (e.g., to express complex dependencies among fluents); • The interactions among agents in [32] are limited to explicit exchange of fluents (through actions of type request and provide); in particular, it is not obvious how to encode cooperative actions-where a shared effect is obtained only when different agents participate in the execution of an action. • The language of [32] takes the perspective of agents operating mostly independently to achieve separate goals, while B MAP allows the modeling of agents cooperating to achieve a shared goal. • The underlying technology used to plan in the action language is radically different-while B MAP is mapped to constraint solving and implemented using constraint logic programming, the language of [32] is mapped to ASP with consistency-restoring rules.…”

Section: Related Workmentioning

confidence: 99%

“…• The language of [32] takes the perspective of agents operating mostly independently to achieve separate goals, while B MAP allows the modeling of agents cooperating to achieve a shared goal. • The underlying technology used to plan in the action language is radically different-while B MAP is mapped to constraint solving and implemented using constraint logic programming, the language of [32] is mapped to ASP with consistency-restoring rules. The language investigated in this work is a variant of the language B originally introduced in [17], as presented in [31].…”

Section: Related Workmentioning

confidence: 99%

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An Investigation of Multi-Agent Planning in CLP

Dovier

Formisano

Pontelli

2010

Fundamenta Informaticae

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This paper explores the use of Constraint Logic Programming (CLP) as a platform for experimenting with planning problems in the presence of multiple interacting agents. The paper develops a novel constraintbased action language, B MAP , that enables the declarative description of large classes of multi-agent and multi-valued domains. B MAP supports several complex features, including combined effects of concurrent and interacting actions, concurrency control, and delayed effects. The paper presents a mapping of B MAP theories to CLP and it demonstrates the effectiveness of an implementation in SICStus Prolog on several benchmark problems. The effort is an evolution of previous research on using CLP for single-agent domains, demonstrating the flexibility of CLP technology to handle the more complex issues of multi-agency and concurrency. * An extended abstract of this work, entitled Representing Multi-Agent Planning in CLP, has appeared in [13].

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Reasoning and Explanation Generation in Ad Hoc Collaboration Between Humans and Embodied AI

Dodampegama,

Sridharan

2024

Lecture Notes in Computer Science

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Reasoning and Planning with Cooperative Actions for Multiagents Using Answer Set Programming

Cited by 11 publications

References 18 publications

An Action Language for Multi-Agent Domains: Foundations

An Action Language for Multi-Agent Domains: Foundations

An Investigation of Multi-Agent Planning in CLP

Reasoning and Explanation Generation in Ad Hoc Collaboration Between Humans and Embodied AI

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