Foundations of Information and Knowledge Systems: 9th by Marc Gyssens, Guillermo Simari

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By Marc Gyssens, Guillermo Simari

This publication constitutes the court cases of the ninth overseas Symposium on Foundations of data and information structures, FoIKS 2016, held in Linz, Austria, in March 2016. The 14 revised complete papers awarded papers have been rigorously reviewed and chosen from 23 submissions. The papers tackle a number of themes resembling reasoning approximately ideals, uncertainty, incompleteness, and inconsistency, inference and challenge fixing, querying and development mining, facing wisdom, logics and complexity.

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However, it holds in a “semantic sense”: closed AFs ensure drawing all such possible claims. Example 23 (Continues from Example 20). By assuming A∗L as the domain of canonical arguments, the argumentation closure renders the closed set of arguments: A = C∗(Θ) = {a, b, c, d, e, f }, where d = {p, q}, p ∧ q , e = {p, ¬p ∨ ¬q}, p ∧ (¬p ∨ ¬q) , and f = {q, ¬p ∨ ¬q}, q ∧ (¬p ∨ ¬q) . For the construction of the set of defeats, we will assume that any argument in Θ is preferred over any other argument which is not in Θ, whereas when considering a pair of arguments where both are either Θ insiders or outsiders, the preference relation will be symmetric.

We now prove that using the compact representation allows for tractable sampling of a possibility distribution. Proposition 1. Constructing C π for π a possibility distribution over S requires an O(n log n) algorithm with n = |S|. Sampling of π based on C π can be done in constant time. 1 A basic belief assignment, or bba, is a function of the form m : 2S → [0, 1] satisfying m(∅) = 0 and A∈2S m(A) = 1. 30 K. Bauters et al. input : a possibility distribution π over S result : a compact representation C π of the possibility distribution π as given in Definition 1 initialise a list C π sort π such that π(s0 ) ≥ π(s2 ) ≥ ...

Markov Decision Processes (MDP) provide such a framework, but can only model uncertainty that can be expressed as probabilities. Possibilistic counterparts of MDPs allow to model imprecise beliefs, yet they cannot accurately represent probabilistic sources of uncertainty and they lack the efficient online solvers found in the probabilistic MDP community. In this paper we advance the state of the art in three important ways. Firstly, we propose the first online planner for possibilistic MDP by adapting the Monte-Carlo Tree Search (MCTS) algorithm.

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