Pub Date : 1900-01-01DOI: 10.4018/978-1-60566-256-5.CH011
Paolo Torroni, Munindar P. Singh, M. Alberti, F. Chesani, M. Gavanelli, E. Lamma, P. Mello
Organizational models often rely on two assumptions: openness and heterogeneity. This is, for instance, the case with organizations consisting of individuals whose behaviour is unpredictable, whose internal structure is unknown, and who do not necessarily share common goals, desires, or intentions. This fact has motivated the adoption of social-based approaches to modelling interaction in organizational models. The idea of social semantics is to abstract away from the agent internals and provide a social meaning to agent message exchanges. In this chapter, we present and discuss two declarative, social semantic approaches for modelling interaction. The first one takes a state-oriented perspective, and models interaction in terms of commitments. The second one adopts a rule-oriented perspective, and models interaction in terms of logical formulae expressing expectations about agent interaction. We use a simple interaction protocol taken from the e-commerce domain to present the functioning and features of the commitment- and expectation-based approaches, and to discuss various forms of reasoning and verification that they accommodate, and how organizational modelling can benefit from them.
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Pub Date : 1900-01-01DOI: 10.4018/978-1-60566-256-5.CH018
S. Fatima, M. Wooldridge
This chapter presents an adaptive organizational policy for multi-agent systems called TRACE. TRACE allows a collection of multi-agent organizations to dynamically allocate tasks and resources between themselves in order to efficiently process and incoming stream of tasks. The tasks have deadlines and their arrival pattern changes over time. Hence, at any instant, some organizations could have surplus resources while others could become overloaded. In order to minimize the number of lost requests caused by an overload, the allocation of resources to organizations is changed dynamically by using ideas from microeconomics. We formally show that TRACE has the ability to adapt to load variations, reduce the number of lost requests, and allocate resources to computations on the basis of their criticality. Furthermore, although the solution generated by TRACE is not always Pareto-optimal, TRACE has the properties of feasibility and monotonicity that make it well suited to time-constrained applications. Finally, we present experimental results to demonstrate the performance of TRACE.
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