## ECSQARU 2017 Accepted Papers

Proceedings available on Springer LNAI (Volume 10369).

Click on the cells to see the abstracts of the papers, the links to the slides, and the Springer links.

*Iterative aggregation of crowdsourced tasks within the belief function theory*.

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*A clustering approach for collaborative filtering under the belief function framework*.

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*Probability measures in Gödel*.

_{Δ}logic_{Δ}(G

_{Δ}) propositional logic. In particular we show that our axioms fully charac- terise finitely additive probability measures over the free finitely generated algebras in the variety constituting the algebraic semantics of G

_{Δ}as integrals of elements of those algebras (represented canonically as algebras of [0, 1]-valued functions), with respect to Borel probability measures.

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*Evaluation of arguments in weighted bipolar graphs*.

*weighted bipolar argumentation graphs*(i.e., graphs whose arguments have basic strengths, and may be both supported and attacked). We introduce axioms that an evaluation method (or semantics) could satisfy. Such axioms are very useful for judging and comparing semantics. We then analyze existing semantics on the basis of our axioms, and finally propose a new semantics for the class of acyclic graphs.

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*A transformation system for unique minimal normal forms of conditional knowledge bases*.

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*Comparison of inference relations defined over different sets of ranking functions*.

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*Efficient policies for stationary possibilistic Markov decision processes*.

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*Algorithms for multi-criteria optimization in possibilistic decision trees*.

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*Structure-based categorisation of Bayesian network parameters*.

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*Debate-based learning game for constructing mathematical proofs*.

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*Incoherence correction and decision making based on generalized credal sets*.

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*Measuring uncertainty in orthopairs*.

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*A two-tiered propositional framework for handling multisource inconsistent information*.

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*Fuzzy weighted attribute combinations based similarity measures*.

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*The complexity of inferences and explanations in probabilistic logic programming*.

_{k}and PP

^{Σk}(for various values of k) and NP

^{PP}are all reached by such computations.

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*The descriptive complexity of Bayesian network specifications*.

*capture*the complexity class PP; that is, any phenomenon that can be simulated with a polynomial time probabilistic Turing machine can be also modeled by such a network. We also show that, by allowing quantification over predicates, the resulting Bayesian network specifications

*capture*the complexity class PP

^{NP}, a result that does not seem to have equivalent in the literature.

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*Complexity of model checking for cardinality-based belief revision operators*.

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*A generic framework to include belief functions in preference handling and multi-criteria decision*.

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*On the Boolean structure of conditional events and its logical counterpart*.

*logic of Boolean conditionals*(LBC) and prove its com- pleteness with respect to the natural semantics induced by the structural properties of the atoms in a conditional algebra as described in the first part. In addition we outline the close connection of LBC with

*preferential consequence relations*, arguably one of the most appreciated systems of non-monotonic reasoning.

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*An Angel-daemon approach to assess the uncertainty in the power of a collectivity to act*.

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*Count queries in probabilistic spatio-temporal knowledge bases with capacity constraints*.

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*Possibilistic MDL: a new possibilistic likelihood based score function for imprecise data*.

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*A Recourse approach for the capacitated vehicle routing problem with evidential demands*.

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*Updating Probabilistic epistemic states in persuasion dialogues*.

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*Decision theory meets linear optimization beyond computation*.

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*From structured to abstract argumentation: assumption-based acceptance via AF reasoning*.

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*Evidential k-NN for link prediction*.

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*Parameter learning algorithms for continuous model improvement using operational data*.

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*Reliable knowledge-based adaptive testing by credal networks*.

*adaptive*test is a computer-based testing technique which adjusts the sequence of questions on the basis of the estimated ability level of the test taker. We suggest the use of credal networks, a general- ization of Bayesian networks based on sets of probability mass functions, to implement adaptive tests exploiting the knowledge of the test devel- oper instead of training on databases of answers. Compared to Bayesian networks, these models might offer higher expressiveness and hence a more reliable modeling of the qualitative expert knowledge. The counter- part is a less straightforward identification of the information-theoretic measure controlling the question-selection and the test-stopping criteria. We elaborate on these issues and propose a sound and computationally feasible procedure. Validation against a Bayesian-network approach on a benchmark about German language proficiency assessments suggests that credal networks can be reliable in assessing the student level and effective in reducing the number of questions required to do it.

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*Monotonicity in Bayesian networks for computerized adaptive testing*.

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*Online fuzzy temporal operators for complex system monitoring*.

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*Reasoning in description logics with typicalities and probabilities of exceptions*.

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*Analogical inequalities*.

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*Boolean analogical proportions – axiomatics and algorithmic complexity issues*.

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*On relating abstract and structured probabilistic argumentation: a case study*.

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*Axiomatization of an importance index for k-ary games*.

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*RankPL: a qualitative probabilistic programming language*.

*RankPL*, a modeling language that can be thought of as a qualitative variant of a probabilistic programming language with a semantics based on Spohn’s ranking theory. Broadly speaking, RankPL can be used to represent and reason about processes that exhibit uncertainty expressible by distinguishing “normal” from “surprising” events. RankPL allows (iterated) revision of rankings over alternative program states and supports various types of reasoning, including abduction and causal inference. We present the language, its denotational semantics, and a number of practical examples. We also discuss an implementation of RankPL that is available for download.

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*Expert opinion extraction from a biomedical database*.

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*Generalized probabilistic modus ponens*.

*from A and “if A then C” infer C*) is one of the most basic inference rules. The probabilistic modus ponens allows for managing uncertainty by transmitting assigned uncertainties from the premises to the conclusion (i.e.,

*from P(A) and P(C|A) infer P(C)*). In this paper, we generalize the probabilistic modus ponens by replacing A by the conditional event A|H. The resulting inference rule involves iterated conditionals (formalized by conditional random quantities) and propagates previsions from the premises to the conclusion. Interestingly, the propagation rules for the lower and the upper bounds on the conclusion of the generalized probabilistic modus ponens coincide with the respective bounds on the conclusion for the (non-nested) probabilistic modus ponens.

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*A first-order logic for reasoning about higher-order upper and lower probabilities*.

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*Ensemble enhanced evidential k-NN classifier through random subspaces*.

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*Exploiting stability for compact representations of independency models*.

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*Solving trajectory optimization problems by influence diagrams*.

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*A semantics for conditionals with default negation*.

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