Argumentation Strategies for Collaborative Plan Resourcing

Emele, Chukwuemeka David; Norman, Timothy J.; Parsons, Simon

doi:10.1007/978-3-642-33152-7_10

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…In norm-governed systems agents are regulated by sets of behavioural expectations, referred to as norms (or policies), which determine the actions an agent is (or is not) allowed to perform in various situations. Delegation in such environments is not just a matter of finding agents that possess the appropriate expertise (or resource); if an agent has the required expertise (or resource) but is operating under a policy that prohibits the performance of that action (or provision of the required resource), it may not take on the delegated task [7]. In such settings, the aim is to identify agents who possess the required expertise (or resource), and whose policies permit the performance of the required action (or provision of the required resource).…”

Section: Task Delegation In Norm-governed Environmentsmentioning

confidence: 99%

Learning strategies for task delegation in norm-governed environments

Emele

Norman

Şensoy

et al. 2012

Auton Agent Multi-Agent Syst

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Section: Task Delegation In Norm-governed Environmentsmentioning

confidence: 99%

Learning strategies for task delegation in norm-governed environments

Emele

Norman

Şensoy

et al. 2012

Auton Agent Multi-Agent Syst

Self Cite

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“…A possible use case for tcla can be identified in modelling information sharing for common resource management. This problem can be instantiated as done in other works like [24,35] as a debate in a multi-agent environment where argumentation techniques are exploited for arriving at desirable outcomes. We start from the scenario proposed by [35], where three counterparts debate on the use of fertilisers for oyster production.…”

Section: Modelling a Debatementioning

confidence: 99%

Timed Concurrent Language for Argumentation: An Interleaving Approach

Bistarelli

Meo

Taticchi

2022

Lecture Notes in Computer Science

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Time is a crucial factor in modelling dynamic behaviours of intelligent agents: activities have a determined temporal duration in a real-world environment, and previous actions influence agents' behaviour. In this paper, we propose a language for modelling concurrent interaction between agents that also allows the specification of temporal intervals in which particular actions occur. Such a language exploits a timed version of Abstract Argumentation Frameworks to realise a shared memory used by the agents to communicate and reason on the acceptability of their beliefs with respect to a given time interval. An interleaving model on a single processor is used for basic computation steps, with maximum parallelism for time elapsing. Following this approach, only one of the enabled agents is executed at each moment. To demonstrate the capabilities of language, we also show how it can be used to model interactions such as debates and dialogue games taking place between intelligent agents. Lastly, we present an implementation of the language that can be accessed via a web interface. Under consideration in Theory and Practice of Logic Programming (TPLP).

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“…In e-commerce systems [3], argumentation is used for solving conflicts that may arise among distributed providers in large scale networks of web services and resources, thus improving the automation level of business processes. In collaborative & cooperative planning [18], argumentation can be combined with other techniques (e.g. machine learning) to help participants collaborate to solve problems by determining what policies are operating by each participant.…”

Section: Argumentation In Collaborative Workmentioning

confidence: 99%

Collaborative Schema Matching Reconciliation

Nguyen

Luong

Miklós

et al. 2013

On the Move to Meaningful Internet Systems: OTM 2013 Conferences

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Abstract. Schema matching is the process of establishing correspondences between the attributes of database schemas for data integration purpose. Although several schema matching tools have been developed, their results are often incomplete or erroneous. To obtain correct attribute correspondences, in practice, human experts edit the mapping results and fix the mapping problems. As the scale and complexity of data integration tasks have increased dramatically in recent years, the reconciliation phase becomes more and more a bottleneck. Moreover, one often needs to establish the correspondences in not only between two but a network of schemas simultaneously. In such reconciliation settings, it is desirable to involve several experts. In this paper, we propose a tool that supports a group of experts to collaboratively reconcile a set of matched correspondences. The experts might have conflicting views whether a given correspondence is correct or not. As one expects global consistency conditions in the network, the conflict resolution might require discussion and negotiation among the experts to resolve such disagreements. We have developed techniques and a tool that allow approaching this reconciliation phase in a systematic way. We represent the expert's views as arguments to enable formal reasoning on the assertions of the experts. We detect complex dependencies in their arguments, guide and present them the possible consequences of their decisions. These techniques thus can greatly help them to overlook the complex cases and work more effectively.

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Argumentation Strategies for Collaborative Plan Resourcing

Cited by 6 publications

References 7 publications

Learning strategies for task delegation in norm-governed environments

Learning strategies for task delegation in norm-governed environments

Timed Concurrent Language for Argumentation: An Interleaving Approach

Collaborative Schema Matching Reconciliation

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