This paper presents an algorithm of adaptation for a case-based reasoning system with cases and domain knowledge represented in the expressive description logic ALC. The principle is to first pretend that the source case to be adapted solves the current target case. This may raise some contradictions with the specification of the target case and with the domain knowledge. The adaptation consists then in repairing these contradictions. This adaptation algorithm is based on an extension of the classical tableau method used for deductive inferences in ALC.

We investigate the problem of influencing the preferences of players within a Boolean game so that, if all players act rationally, certain desirable outcomes will result. The way in which we influence preferences is by overlaying games with taxation schemes. In a Boolean game, each player has unique control of a set of Boolean variables, and the choices available to the player correspond to the possible assignments that may be made to these variables. Each player also has a goal, represented by a Boolean formula, that they desire to see satisfied. Whether or not a player’s goal is satisfied will depend both on their own choices and on the choices of others, which gives Boolean games their strategic charac- ter. We extend this basic framework by introducing an external principal who is able to levy a taxation scheme on the game, which imposes a cost on every possible action that a player can choose. By designing a taxation scheme appropriately, it is possible to perturb the preferences of the players, so that they are incentivised to choose some equilibrium that would not otherwise be chosen. After motivating and formally presenting our model, we explore some issues surrounding it, including the complexity of finding a taxation scheme that implements some socially desirable outcome, and then discuss desirable properties of taxation schemes.

In this paper we propose a mechanism that encourages agents, participating in an open MAS, to follow a desirable behaviour, by introducing modifications in the environment. This mechanism is deployed by using an infrastructure based on institutional agents called incentivators. Each external agent is assigned to an incentivator that is able to discover its preferences, and to learn the suitable modifications in the environment, in order to improve the global utility of a system in response to inadequate design or changes in the population of participating agents. The mechanism is evaluated in a p2p scenario.

Lifted message passing algorithms exploit repeated structure within a given graphical model to answer queries efficiently. Given evidence, they construct a lifted network of supernodes and superpotentials corresponding to sets of nodes and potentials that are indistinguishable given the evidence. Recently, efficient algorithms were presented for updating the structure of an existing lifted network with incremental changes to the evidence. In the inference stage, however, current algorithms need to construct a separate lifted network for each evidence case and run a modified message passing algorithm on each lifted network separately. Consequently, symmetries across the inference tasks are not exploited. In this paper, we present a novel lifted message passing technique that exploits symmetries across multiple evidence cases. The benefits of this multi-evidence lifted inference are shown for several important AI tasks such as computing personalized PageRanks and Kalman filters via multi-evidence lifted Gaussian belief propagation.

We propose a nonmonotonic extension of low complexity Description Logics EL⊥ and DL-Litecore for reasoning about typicality and defeasible properties. The resulting logics are called EL⊥ T min and DL-Litec T min . Concerning DL-Litec T min , we prove that entailment is in \Pi^p_2. With regard to EL⊥ T min , we first show that entailment remains EXPTIME-hard. Next we consider the known fragment of Left Local EL⊥ T min and we prove that the complexity of entailment drops to \Pi^p_2.

An online double auction mechanism for dynamic environments, especially dynamic has to match sellers and buyers dynamically and calculate double auctions. After a brief review of related a payment for each matched trader without knowing work, we specify the problem we are tackling, and about future orders. Such uncertainty is more challenging for then briefly outline our research plan, the results we double auction mechanism design because modelling traders' have achieved to date, and the ongoing directions.

We consider the following sequential allocation process. A benevolent central authority has to allocate a set of indivisible goods to a set of agents whose preferences it is totally ignorant of. We consider the process of allocating objects one after the other by designating an agent and asking her to pick one of the objects among those that remain. The problem consists in choosing the "best" sequence of agents, according to some optimality criterion. We assume that agents have additive preferences over objects. The choice of an optimality criterion depends on three parameters: how utilities of objects are related to their ranking in an agent's preference relation; how the preferences of different agents are correlated; and how social welfare is defined from the agents' utilities. We address the computation of a sequence maximizing expected social welfare under several assumptions. We also address strategical issues.

Clause learning is a technique used by back-tracking-based propositional satisfiability solvers, where some clauses obtained by analysis of conflicts are added to the formula during backtracking. It has been observed empirically that clause learning does not significantly improve the performance of a solver when restricted to learning clauses of small width only. This experience is supported by lower bound theorems. It is shown that lower bounds on the runtime of width-restricted clause learning follow from lower bounds on the width of resolution proofs. This yields the first lower bounds on width-restricted clause learning for formulas in 3-CNF.

How obliged can we be to AI, and how much danger does it pose us? A surprising proportion of our society holds exaggerated fears or hopes for AI, such as the fear of robot world conquest, or the hope that AI will indefinitely perpetuate our culture. These misapprehensions are symptomatic of a larger problem—a confusion about the nature and origins of ethics and its role in society. While AI technologies do pose promises and threats, these are not qualitatively different from those posed by other artifacts of our culture which are largely ignored: from factories to advertising, weapons to political systems. Ethical systems are based on notions of identity, and the exaggerated hopes and fears of AI derive from our cultures having not yet accommodated the fact that language and reasoning are no longer uniquely human. The experience of AI may improve our ethical intuitions and self-understanding, potentially helping our societies make better-informed decisions on serious ethical dilemmas.

Designing dialog policies for voice-enabled interfaces is a tailoring job that is most often left to natural language processing experts. This job is generally redone for every new dialog task because cross-domain transfer is not possible. For this reason, machine learning methods for dialog policy optimization have been investigated during the last 15 years. Especially, reinforcement learning (RL) is now part of the state of the art in this domain. Standard RL methods require to test more or less random changes in the policy on users to assess them as improvements or degradations. This is called on policy learning. Nevertheless, it can result in system behaviors that are not acceptable by users. Learning algorithms should ideally infer an optimal strategy by observing interactions generated by a non-optimal but acceptable strategy, that is learning off-policy. In this contribution, a sample-efficient, online and off-policy reinforcement learning algorithm is proposed to learn an optimal policy from few hundreds of dialogues generated with a very simple handcrafted policy.