By Matteo Baldoni, Ulle Endriss, Andrea Omicini, Paolo Torroni
This ebook constitutes the completely refereed post-proceedings of the 3rd foreign Workshop on Declarative Agent Languages and applied sciences, DALT 2005, held in Utrecht, The Netherlands in July 2005 as an linked occasion of AAMAS 2005, the most overseas convention on self sufficient brokers and multi-agent systems.
The 14 revised complete papers awarded have been conscientiously chosen in the course of rounds of reviewing and development for inclusion within the ebook. The papers are prepared in topical sections on agent programming and ideology, architectures and good judgment programming, wisdom illustration and reasoning, and coordination and version checking.
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Extra info for Declarative Agent Languages and Technologies III: Third International Workshop, DALT 2005, Utrecht, The Netherlands, July 25, 2005, Selected and Revised
The new probability of b1 can be calculated since mb1 ⊕ mb2 ⊕ mb1 = mb1 ⊕ mb1 ⊕ mb2 = (mb1 ⊕ mb1 ) ⊕ mb2 . Combining mb1 and mb1 leads to a single support function with a mass value of p1 +p3 −p1 ·p3 for the set X = maxΩ (b1 ), therefore we can update the probability of b1 in the belief base to p1 + p3 − p1 · p3 . Furthermore, we can test (query) if a proposition ϕ can be derived from a belief base Γ . In section 2, we discussed the belief and plausibility functions (deﬁned in terms of a certain mass function) that return the total mass assigned to models of ϕ and the total mass that is not assigned to models of the negation of ϕ.
First, epistemic axioms and their correspondence with sets of legal general epistemic states are defined. Then, conditions on these sets are defined, and it is shown that the GKSSs of a given set of epistemic axioms – being (essentially) the Cartesian product of the corresponding sets of legal general states – exhibit the finite model property if the sets of legal general states fulfil the conditions. 4. 1 Epistemic Axioms Not all formulae in EL should be considered as candidates for describing epistemic properties.
Meyer. A programming language for cognitive agents : Goal directed 3APL. In Proceedings of the First Workshop on Programming Multiagent Systems (ProMAS03), 2003. 6. N. de C. Ferreira, M. Fisher, and W. van der Hoek. A simple logic for reasoning about uncertainty. In Proceedings of the ESSLLI’04 Student Session, pages 61–71, 2004. 7. R. Y. Halpern. Reasoning about knowledge and probablility. Journal of the ACM, 41:340–367, 1994. 8. M. Fattorosi-Barnaba and G. Amati. Studio logica. 46:383–393, 1987.