By Alexander Artikis, Marek Sergot, Jeremy Pitt (auth.), Fausto Giunchiglia, James Odell, Gerhard Weiß (eds.)

Over the prior 3 a long time, software program engineers have derived a steadily larger realizing of the features of complexity in software program. it truly is now commonly recognized thatinteraction is one of the most crucial unmarried char- teristic of advanced software program. software program architectures that include many dyna- cally interacting elements, every one with their very own thread of keep watch over, and eng- ing in advanced coordination protocols, tend to be orders of value extra advanced to properly and e?ciently engineer than those who easily compute a functionality of a few enter via a unmarried thread of keep an eye on. regrettably, it seems that many (if no longer such a lot) real-world purposes have accurately those features. therefore, an incredible study subject in c- puter technological know-how over a minimum of the prior 20 years has been the advance of instruments and methods to version, comprehend, and enforce structures within which interplay is the norm. certainly, many researchers now think that during destiny computation itself might be understood as chie?y a means of interaction.

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Extra resources for Agent-Oriented Software Engineering III: Third International Workshop, AOSE 2002 Bologna, Italy, July 15, 2002 Revised Papers and Invited Contributions

Example text

In this paper, we reformulate the theory of multi-agent model checking in order to allow for the use of symbolic model checking techniques [19]. The main feature of symbolic model checking is the ability to manipulate sets of states rather than single states. Such sets are efficiently represented and transformed by means of Binary Decision Diagrams (Bdds) [8]. Symbolic model checking techniques can often lead to dramatic improvements over the explicit-state approach. The resulting theory allows for a direct reuse of the standard temporal logic model checking techniques, and the related tools, and extends them naturally to deal with the multi-agent aspects of the logic.

The set of implicit bridge atoms Implα,i = {Bi φ ∈ Lα }\Explα,i may be infinite. Implicit bridge atoms are given semantics in terms of the explicit ones, by means of compatibility relations. A compatibility relation Cα,i ⊆ Explα,i × Sαi , constrains the truth of explicit bridge atoms of a view α to the truth values in the views αi. Depending on the “kind” of bridge operator being considered, the compatibility relation may have different properties. In Section 3 we define what constraints the compatibility relations need to satisfy so as to give bridge operators the correct semantics of belief operators.

Similarly, [17] proposes a translation methods from Agent UML protocol diagrams into Promela, the input language of Spin, to check reacability properties of agent protocols. All this work is essentially limited to model and validate those aspects of multiagents systems expressible by standard validation methodologies for concurrent reactive systems, emerged in mainstreem computer science. On the other hand, both traditional [22] and more recent [14] specification methodologies for multiagent systems employ knowldge level notions, such as belifs, goals, intentions.

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