By Matteo Baldoni, Ulle Endriss, Andrea Omicini, Paolo Torroni
This publication constitutes the completely refereed post-proceedings of the 3rd overseas 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 foreign convention on independent brokers and multi-agent systems.
The 14 revised complete papers offered have been rigorously chosen in the course of rounds of reviewing and development for inclusion within the e-book. The papers are prepared in topical sections on agent programming and ideology, architectures and common sense programming, wisdom illustration and reasoning, and coordination and version checking.
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Extra resources for Declarative Agent Languages and Technologies III: Third International Workshop, DALT 2005, Utrecht, The Netherlands, July 25, 2005, Selected and Revised
01 X = ’killer is Paul’ ⎪ ⎪ ⎩ 0 X =∅ 3 To be more exact, Yager diﬀerentiates between ground probabilities q(X) and basic probabilities m(X). The empty set can have a q(∅) ≥ 0. When combining, these ground probabilities are used and the mass is attributed after the combination, where m(X) = q(X) for X = ∅, and m(Ω) = q(Ω) + q(∅). Modelling Uncertainty in Agent Programming 25 Table 2. 495 0 We can summarise these approaches (Dempster, Yager/Smets and Jøsang) using the ‘murder case’ example, as shown in table 2.
6 Bel(maxΩ (φ)) = pφ Bel(maxΩ (¬φ)) = Bel(Ω\maxΩ (φ)) = 1 − pφ Bel(maxΩ (φ ∧ ψ)) = pφ · pψ Bel(maxΩ (φ ∨ ψ)) = pφ + pψ − pφ · pψ See section 2. Modelling Uncertainty in Agent Programming 31 Proof. The proof of the ﬁrst clause is as follows: Bel(maxΩ (ϕ)) is deﬁned as the sum of all mass values that are deﬁned for a certain subset of hypotheses in Ω that are models of ϕ. , ⎧ if X = maxΩ (φ) ⎨ pφ mφ (X) = 1 − pφ if X = Ω ⎩ 0 otherwise If the belief base is, however, a combination of two mass functions, say mφ and mχ , then maxΩ (φ) and maxΩ (φ ∧ χ) are the only focal elements of mφ ⊕ mχ that consist of models of φ.
Combination of Evidence in Dempster-Shafer Theory. PhD thesis, Binghamton University, 2002. 14. G. Shafer. A mathematical theory of evidence. Princeton Univ. Press, Princeton, NJ, 1976. 15. P. Smets. The combination of evidence in the transferable belief model. IEEE Pattern Analysis and Machine Intelligence, 12:447–458, 1990. 16. W. van der Hoek. Some considerations on the logic PFD. Journal of Applied Non Classical Logics, 7:287–307, 1997. 17. M. Verbeek. 3APL as programming language for cognitive robotics.