L'intégration des connaissances ontologiques pour l'apprentissage des réseaux bayesiens causaux

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SemCaDo: a serendipitous causal discovery algorithm for ontology evolution

International audienceWith the rising need to reuse the existing knowledge when learning Causal Bayesian Networks (CBNs), the ontologies can supply valuable semantic information to make further interesting discoveries with the minimum expected cost and effort. In this paper, we propose a cyclic approach in which we make use of the ontology in an interchangeable way. The first direction involves the integration of semantic knowledge to anticipate the optimal choice of experimentations via a serendipitous causal discovery strategy. The second complementary direction concerns an enrichment process by which it will be possible to reuse these causal discoveries, support the evolving character of the semantic background and make an ontology evolution

Possibilistic sequential decision making

International audienceWhen the information about uncertainty cannot be quantified in a simple, probabilistic way, the topic of possibilistic decision theory is often a natural one to consider. The development of possibilistic decision theory has lead to the proposition a series of possibilistic criteria, namely: optimistic and pessimistic possibilistic qualitative criteria [7], possibilistic likely dominance [2] and [9], binary possibilistic utility [11] and possibilistic Choquet integrals [24]. This paper focuses on sequential decision making in possibilistic decision trees. It proposes a theoretical study on the complexity of the problem of finding an optimal strategy depending on the monotonicity property of the optimization criteria – when the criterion is transitive, this property indeed allows a polytime solving of the problem by Dynamic Programming. We show that most possibilistic decision criteria, but possibilistic Choquet integrals, satisfy monotonicity and that the corresponding optimization problems can be solved in polynomial time by Dynamic Programming. Concerning the possibilistic likely dominance criteria which is quasi-transitive but not fully transitive, we propose an extended version of Dynamic Programming which remains polynomial in the size of the decision tree. We also show that for the particular case of possibilistic Choquet integrals, the problem of finding an optimal strategy is NP-hard. It can be solved by a Branch and Bound algorithm. Experiments show that even not necessarily optimal, the strategies built by Dynamic Programming are generally very good

Egalitarian Collective Decision Making under Qualitative Possibilistic Uncertainty: Principles and Characterization

International audienceThis paper raises the question of collective decision making under possibilistic uncertainty; We study four egalitarian decision rules and show that in the context of a possibilistic representation of uncertainty, the use of an egalitarian collective utility function allows to get rid of the Timing Effect. Making a step further, we prove that if both the agents’ preferences and the collective ranking of the decisions satisfy Dubois and Prade’s axioms (1995), and particularly risk aversion, and Pareto Unanimity, then the egalitarian collective aggregation is compulsory. This result can be seen as an ordinal counterpart of Harsanyi’s theorem (1955)

The Communication Burden of Single Transferable Vote, in Practice

We study single-winner STV from the point of view of communication. First, we assume that voters give, in a single shot, their top-k alternatives; we define a version of STV that works for such votes, and we evaluate empirically the extent to which it approximates the standard STV rule. Second, we evaluate empirically the communication cost of the protocol for STV defined by Conitzer and Sandholm (2005) and some of its improvements

Possibilistic Conditional Preference Networks

International audienceThe paper discusses the use of product-based possibilistic networks for representing conditional preference statements on discrete variables. The approach uses non-instantiated possibility weights to define conditional preference tables. Moreover, additional information about the relative strengths of symbolic weights can be taken into account. It yields a partial preference order among possible choices corresponding to a symmetric form of Pareto ordering. In the case of Boolean variables, this partial ordering coincides with the inclusion between the sets of preference statements that are violated. Furthermore, this graphical model has two logical counterparts in terms of possibilistic logic and penalty logic. The flexibility and the representational power of the approach are stressed. Besides, algorithms for handling optimization and dominance queries are provided

Single Transferable Vote: Incomplete Knowledge and Communication Issues

International audienceSingle Transferable Vote (STV) is used in large political elections around the world. It is easy to understand and has desirable normative properties such as clone-proofness. However, voters need to report full rankings, which can make it less practical than plurality voting. We study ways to minimize the amount of communication required to use single-winner STV. In the first part of the paper, voters are assumed to report their top-k alternatives in a single shot. We empirically evaluate the extent to which STV with truncated ballots approximates STV with full information. We also study the computational complexity of the possible winner problem for top-k ballots. For $k=1$, it can be solved in polynomial time, but is NP-complete when $k\geq 2$. In the second part, we consider interactive communication protocols for STV. Building on a protocol proposed by Conitzer and Sandholm (2005), we show how we can reduce the amount of communication required in practice. We then study empirically the average communication complexity of these protocols, based on randomly generated profiles, and on real-world election data. Our conclusion is that STV needs, in practice, much less information than in the worst case