Constraint satisfaction as a tool for modeling and checking feasibility of multiagent commitments

Multi-agents systems are composed of autonomous and possibly heterogeneous software agents that act according to their own interests. Some coordination mechanism must be adopted to ensure a proper functioning of the whole system. Norms can be viewed as a powerful means to regulate and influence the behaviour of the agents by specifying, for instance, obligations, permissions, or prohibitions in a given context. A critical issue that must be considered in a system governed by multiple norms is the possible existence of normative conflicts. A conflict between norms is a situation in which the fulfilment of a norm causes a violation of another one. In this paper, we present several techniques that have been proposed to detect and resolve normative conflicts in multi-agent systems. Our aim is to organize the literature, present a classification of the techniques found, and provide a means to compare alternative approaches dealing with normative conflicts.

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“…However, in other work [Günay and Yolum, 2013a], constraint satisfaction methods were applied to deal with it.…”

Section: Anamentioning

confidence: 99%

Detection and resolution of normative conflicts in multi-agent systems: a literature survey

Santos

Zahn

Silvestre

et al. 2017

Auton Agent Multi-Agent Syst

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“…We also analyzed five additional domains that feature specialized agents with different planning capabilities: Depots, Elevators, Logistics, Openstacks and Woodworking. The features of these domains give rise to complex, tightly-coupled tasks that require interactions or commitments [13] among agents in order to be solved.…”

Section: Tightly-coupled Domainsmentioning

confidence: 99%

FMAP: Distributed cooperative multi-agent planning

2014

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This paper proposes FMAP (Forward MultiAgent Planning), a fully-distributed multi-agent planning method that integrates planning and coordination. Although FMAP is specifically aimed at solving problems that require cooperation among agents, the flexibility of the domain-independent planning model allows FMAP to tackle multi-agent planning tasks of any type. In FMAP, agents jointly explore the plan space by building up refinement plans through a complete and flexible forward-chaining partial-order planner. The search is guided by h DT G , a novel heuristic function that is based on the concepts of Domain Transition Graph and frontier state and is optimized to evaluate plans in distributed environments. Agents in FMAP apply an advanced privacy model that allows them to adequately keep private information while communicating only the data of the refinement plans that is relevant to each of the participating agents. Experimental results show that FMAP is a general-purpose approach that efficiently solves tightly-coupled domains that have specialized agents and cooperative goals as well as loosely-coupled problems. Specifically, the empirical evaluation shows that FMAP outperforms current MAP systems at solving complex planning tasks that are adapted from the International Planning Competition benchmarks.

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“…A social commitment SC is a public commitment made by an agent (called the debtor), and directed towards a set of agents (called creditor), indicating that some fact is true or some action will be performed [10]. Nowadays, social commitments have been extensively and effectively used in a variety of areas ranging from developing artificial institutions [11], verifying process communication [12], and modeling multi-agent interaction [13] and so on. The definition of social commitment as follows:…”

Section: Social Commitmentmentioning

confidence: 99%