Possibilistic Reasoning in Multi-Context Systems: Preliminary Report

Jin, Yifan; Wang, Kewen; Wen, Lian

doi:10.1007/978-3-642-32695-0_18

Cited by 7 publications

(23 citation statements)

References 8 publications

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“…Research in representing contexts and information flow between contexts has gained much attention recently in artificial intelligence [11,4,7,8,5,13] as well as in applications such as requirements engineering [10,15,14].…”

Section: Introductionmentioning

confidence: 49%

Preferential Multi-Context Systems

Wang

Wen

2016

International Journal of Approximate Reasoning

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Multi-context systems (MCS) presented by Brewka and Eiter can be considered as a promising way to interlink decentralized and heterogeneous knowledge contexts. In this paper, we propose preferential multicontext systems (PMCS), which provide a framework for incorporating a total preorder relation over contexts in a multi-context system. In a given PMCS, its contexts are divided into several parts according to the total preorder relation over them, moreover, only information flows from a context to ones of the same part or less preferred parts are allowed to occur. As such, the first l preferred parts of an PMCS always fully capture the information exchange between contexts of these parts, and then compose another meaningful PMCS, termed the l-section of that PMCS. We generalize the equilibrium semantics for an MCS to the (maximal) l ≤ -equilibrium which represents belief states at least acceptable for the lsection of an PMCS. We also investigate inconsistency analysis in PMCS and related computational complexity issues.

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Section: Introductionmentioning

confidence: 49%

Preferential Multi-Context Systems

Wang

Wen

2016

International Journal of Approximate Reasoning

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“…Recently, Jin et al proposed a framework for possibilistic reasoning in Multi-Context Systems, which they called possibilistic MCS [26]. This has been so far the only attempt to model uncertainty in MCS.…”

Section: Possibilistic Reasoning In Mcsmentioning

confidence: 99%

“…The first one is non-monotonic MCS [14], the main characteristics of which are that each context may use a different formalism to represent knowledge, and that bridge rules have the form of non-monotonic logic programming rules, which combine elements from different contexts in their body. The second variant is possibilistic MCS [26], an extension of non-monotonic MCS that explicitly models uncertainty in the contexts and the bridge rules. Four advantages of our proposed approach are: (a) It is able to handle agents that use different knowledge representation formalisms; (b) the format of the bridge rules allows modeling different types of relationships between agents such as inter-dependencies, conflicting goals and constraints; (c) the possibilistic extension of MCS enables modeling uncertainty in the agents' actions; (d) MCS is a well-studied model and there are both centralized and distributed reasoning algorithms and tools that can be used to reason with it.…”

Section: Introductionmentioning

confidence: 99%

Contextual and Possibilistic Reasoning for Coalition Formation

Bikakis

Caire

2020

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In multi-agent systems, agents often need to cooperate and form coalitions to fulfil their goals, for example by carrying out certain actions together or by sharing their resources. In such situations, some questions that may arise are: Which agent(s) to cooperate with? What are the potential coalitions in which agents can achieve their goals? As the number of possibilities is potentially quite large, how to automate the process? And then, how to select the most appropriate coalition, taking into account the uncertainty in the agents’ abilities to carry out certain tasks? In this article, we address the question of how to identify and evaluate the potential agent coalitions, while taking into consideration the uncertainty around the agents’ actions. Our methodology is the following: We model multi-agent systems as Multi-Context Systems, by representing agents as contexts and the dependencies among agents as bridge rules. Using methods and tools for contextual reasoning, we compute all possible coalitions with which the agents can fulfil their goals. Finally, we evaluate the coalitions using appropriate metrics, each corresponding to a different requirement. To demonstrate our approach, we use an example from robotics.

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“…However, the semantics of aMCS is highly operational, which makes it rather difficult to see how the declarative notion of diagnosis could be reasonably extended to this setting. Jin, Wang, and Wen (2012) developed a framework for possibilistic reasoning in MCS termed poss-MCS where each context is a possibilistic logic program and information exchange is realized using possibilistic bridge rules. Intuitively, every (bridge) rule of a possibilistic context is an ordinary (bridge) rule that has an associated degree of necessity α ∈ [0, 1].…”

Section: Further Mcs Extensionsmentioning

confidence: 99%

Preference-Based Inconsistency Management in Multi-Context Systems

Eiter¹,

Weinzierl²

2017

jair

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Multi-Context Systems (MCS) are a powerful framework for interlinking possibly heterogeneous, autonomous knowledge bases, where information can be exchanged among knowledge bases by designated bridge rules with negation as failure. An acknowledged issue with MCS is inconsistency that arises due to the information exchange. To remedy this problem, inconsistency removal has been proposed in terms of repairs, which modify bridge rules based on suitable notions for diagnosis of inconsistency. In general, multiple diagnoses and repairs do exist; this leaves the user, who arguably may oversee the inconsistency removal, with the task of selecting some repair among all possible ones. To aid in this regard, we extend the MCS framework with preference information for diagnoses, such that undesired diagnoses are filtered out and diagnoses that are most preferred according to a preference ordering are selected. We consider preference information at a generic level and develop meta-reasoning techniques on diagnoses in MCS that can be exploited to reduce preference-based selection of diagnoses to computing ordinary subset-minimal diagnoses in an extended MCS. We describe two meta-reasoning encodings for preference orders: the first is conceptually simple but may incur an exponential blowup. The second is increasing only linearly in size and based on duplicating the original MCS. The latter requires nondeterministic guessing if a subset-minimal among all most preferred diagnoses should be computed. However, a complexity analysis of diagnoses shows that this is worst-case optimal, and that in general, preferred diagnoses have the same complexity as subset-minimal ordinary diagnoses. Furthermore, (subset-minimal) filtered diagnoses and (subset-minimal) ordinary diagnoses also have the same complexity.

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Possibilistic Reasoning in Multi-Context Systems: Preliminary Report

Cited by 7 publications

References 8 publications

Preferential Multi-Context Systems

Preferential Multi-Context Systems

Contextual and Possibilistic Reasoning for Coalition Formation

Preference-Based Inconsistency Management in Multi-Context Systems

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