By Boi Faltings (auth.), Joaquim Filipe, Ana Fred (eds.)

This booklet constitutes the completely refereed post-conference lawsuits of the 3rd overseas convention on brokers and synthetic Intelligence, ICAART 2011, held in Rome, Italy, in January 2011. The 26 revised complete papers provided including invited paper have been rigorously reviewed and chosen from 367 submissions. The papers are geared up in topical sections on man made intelligence and on agents.

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Extra info for Agents and Artificial Intelligence: Third International Conference, ICAART 2011, Rome, Italy, January, 28-30, 2011. Revised Selected Papers

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G. a timeout) has occurred. All replies to a sent message that are received after the synchronization was successfully achieved are ignored. – A special variable MaxMessages in the protocol context gives the number of how many messages were actually sent in the start message flow. With this basic machinery it is already possible to design a large number of complex protocols. A limitation of the presented concepts is that it is not possible to model protocols where messages are sent out to more than one actor while the sender waits for the replies to these messages in a concurrent manner.

The core of DSML4MAS is defined by the metamodel PIM4Agents that includes platform independent concepts for the design of MAS. PIM4Agents is separated into 12 major aspects for MAS design. The general framework proposes a plugin architecture where these different aspects can be replaced or refined in a flexible manner. In the discussion of the PIM4Agents metamodel the article concentrates on the Interaction aspect because it is an obvious choice when it comes to reuse of model fragments in MAS design.

48) 2I In practice, bounds (47) and (48) can be significantly tightened by using an estimate B in the place of the most pessimistic B. The estimate B can be found by performing just one fold of cross-validation (instead of n folds) and bounding B by: mean error on the testing set plus a square root implied by the Chernoff inequality: B ≤ Remp (ωI ) + B − ln ηB , 2I (49) where ηB is an imposed small probability that (49) is not true. The reasoning behind this remark is that in practice, typical learning algorithms rarely produce functions f (x, ωI ), in the process of ERM, having high maximal errors.

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