Following the recent trend of studying the theory of belief revision under the Horn fragment of propo- sitional logic this paper develops a fully charac- terised Horn contraction which is analogous to the traditional transitively relational partial meet contraction [Alchourron et al., 1985]. This Horn con- traction extends the partial meet Horn contraction studied in [Delgrande and Wassermann, 2010] so that it is guided by a transitive relation that models the ordering of plausibility over sets of beliefs.

We study uniform interpolation and forgetting in the description logic ALC. Our main results are model-theoretic characterizations of uniform interpolants and their existence in terms of bisimulations, tight complexity bounds for deciding the existence of uniform interpolants, an approach to computing interpolants when they exist, and tight bounds on their size. We use a mix of model-theoretic and automata-theoretic methods that, as a by-product, also provides charachterizations of, and decision procedures for, conservative extensions.

There are many significant research projects focused on providing semantic web repositories that are scalable and efficient. However, the true value of the semantic web architecture is its ability to represent meaningful knowledge and not just data. Therefore, a semantic web knowledge base should do more than retrieve collections of triples. We propose RDFKB (Resource Description Knowledge Base), a complete semantic web knowledge case. RDFKB is a solution for managing, persisting and querying semantic web knowledge. Our experiments with real world and synthetic datasets demonstrate that RDFKB achieves superior query performance to other state-of-the-art solutions. The key features of RDFKB that differentiate it from other solutions are: 1) a simple and efficient process for data additions, deletions and updates that does not involve reprocessing the dataset; 2) materialization of inferred triples at addition time without performance degradation; 3) materialization of uncertain information and support for queries involving probabilities; 4) distributed inference across datasets; 5) ability to apply alignments to the dataset and perform queries against multiple sources using alignment. RDFKB allows more knowledge to be stored and retrieved; it is a repository not just for RDF datasets, but also for inferred triples, probability information, and lineage information. RDFKB provides a complete and efficient RDF data repository and knowledge base.

The environment is an essential component of multi-agent systems and is often used to coordinate the behaviour of individualagents. Recently many languages have been proposed to specify and implement multi-agent environments in terms of social and normative concepts. In this paper, we first introduce a formal setting of multi-agent environment which abstracts from concrete specification languages. We extend this formal setting with norms and sanctions and show how concepts from mechanism design can be used to formally analyse and verify whether specific normative behaviours can be enforced (or implemented) if agents follow their subjective preferences. We also consider complexity issues of associated problems.

The minimization problem for propositional formulas is an important optimization problem in the second level of the polynomial hierarchy. In general, the problem is Sigma-2-complete under Turing reductions, but restricted versions are tractable. We study the complexity of minimization for formulas in two established frameworks for restricted propositional logic: The Post framework allowing arbitrarily nested formulas over a set of Boolean connectors, and the constraint setting, allowing generalizations of CNF formulas. In the Post case, we obtain a dichotomy result: Minimization is solvable in polynomial time or coNP-hard. This result also applies to Boolean circuits. For CNF formulas, we obtain new minimization algorithms for a large class of formulas, and give strong evidence that we have covered all polynomial-time cases.

In this paper we present a new algorithm for generatively learning the structure of Markov Logic Networks. This algorithm relies on a graph of predicates, which summarizes the links existing between predicates and on relational information between ground atoms in the training database. Candidate clauses are produced by means of a heuristical variabilization technique. According to our first experiments, this approach appears to be promising.

As explained by Axelrod in his seminal work An Evolutionary Approach to Norms , punishment is a key mechanism to achieve the necessary social control and to impose social norms in a self-regulated society. In this paper, we distinguish between two enforcing mechanisms. i.e. punishment and sanction , focusing on the specific ways in which they favor the emergence and maintenance of cooperation. The key research question is to find more stable and cheaper mechanisms for norm compliance in hybrid social environments (populated by humans and computational agents). To achieve this task, we have developed a normative agent able to punish and sanction defectors and to dynamically choose the right amount of punishment and sanction to impose on them ( Dynamic Adaptation Heuristic ). The results obtained through agent-based simulation show us that sanction is more effective and less costly than punishment in the achievement and maintenance of cooperation and it makes the population more resilient to sudden changes than if it were enforced only by mere punishment.

The classic Gibbard-Satterthwaite Theorem establishes that only dictatorial voting rules are strategy-proof; under any other voting rule, players have an incentive to lie about their true preferences. We consider a new approach for circumventing this result: we consider randomized voting rules that only approximate a deterministic voting rule and only are approximately strategy-proof. We show that any deterministic voting rule can be approximated by an approximately strategy-proof randomized voting rule, and we provide asymptotically tight lower bounds on the parameters required by such voting rules.

The variability of human behavior during plan execution poses a difficult challenge for human-robot teams. In this paper, we use the concepts of theory of mind to enable robots to account for two sources of human variability during team operation. When faced with an unexpected action by a human teammate, a robot uses a simulation analysis of different hypothetical cognitive models of the human to identify the most likely cause for the human's behavior. This allows the cognitive robot to account for variances due to both different knowledge and beliefs about the world, as well as different possible paths the human could take with a given set of knowledge and beliefs. An experiment showed that cognitive robots equipped with this functionality are viewed as both more natural and intelligent teammates, compared to both robots who either say nothing when presented with human variability, and robots who simply point out any discrepancies between the human's expected, and actual, behavior. Overall, this analysis leads to an effective, general approach for determining what thought process is leading to a human's actions.

Human environments are challenging for robots, which need to be trainable by lay people and learn new behaviours rapidly without disrupting much the ongoing activity. A system that integrates AI techniques for planning and learning is here proposed to satisfy these strong demands. The approach rapidly learns planning operators from few action experiences using a competitive strategy where many alternatives of cause-effect explanations are evaluated in parallel, and the most successful ones are used to generate the operators. The success of a cause-effect explanation is evaluated by a probabilistic estimate that compensates the lack of experience, producing more confident estimations and speeding up the learning in relation to other known estimates. The system operates without task interruption by integrating in the planning-learning loop a human teacher that supports the planner in making decisions. All the mechanisms are integrated and synchronized in the robot using a general decision-making framework. The feasibility and scalability of the architecture are evaluated in two different robot platforms: a Stäubli arm, and the humanoid ARMAR III.