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09:00 | Datalog+/â€“: Questions and Answers SPEAKER: Georg Gottlob ABSTRACT. Datalog+/? is a family of languages for knowledge representation and reasoning. These languages extend Datalog with features such as existential quantifiers, equalities, and the falsum in rule heads, and, at the same time, applies restrictions to achieve decidability and tractability. After a general overview of the Datalog+/? family, this talk will focus on more recent issues. Among other things, I will report on the combination of the two main decidability paradigms guardedness and stickiness, yielding the Tame Fragment, and on incorporating non-monotonic negation and disjunction into Datalog+/–: . I will also report about a special version of Datalog+/? suitable for reasoning with reverse-engineered UML class diagrams, and about the TriQ language that expresses SPARQL with entailment regimes. |

10:45 | Dynamic Causal Calculus SPEAKER: Alexander Bochman ABSTRACT. We introduce dynamic causal calculus, a nonmonotonic formalism that can be viewed as a direct logical counterpart of the action description language C+ from \cite{GLMT01}. We formulate a nonmonotonic semantics of the associated causal language, and compare this semantics with the indirect, two-stage semantics for C+, given in \cite{GLMT01}. It will be shown, in particular, that the suggested semantics allows us to alleviate syntactic distinctions between propositional atoms, maintained by C+, as well as type restrictions imposed on its causal laws. We will describe also a logical formalism of dynamic causal inference that constitutes a complete description of the logic that is adequate for this dynamic calculus. |

11:15 | Appropriate Causal Models and Stability of Causation SPEAKER: Joseph Halpern ABSTRACT. Causal models defined in terms structural equations have proved to be quite a powerful way of representing knowledge regarding causality. However, a number of authors have designed examples where it seems that the Halpern-Pearl (HP) definition of causality gives intuitively unreasonable answers. As has been pointed out by many authors, what gets counted as a cause is quite dependent on the choice of variables in the model. Here it is shown that in all the counterexamples suggested, there are two possible stories consistent with the counterexample, where intuitions regarding causality are quite different. By adding additional variables, we can disambiguate the stories; moreover, in the resulting causal models, the HP definition of causality gives the intuitively correct answer. The examples not only give insight into the modeling process, they also highlight some aspects of the definition of causality itself. Specifically, by extending one of the examples, it can be shown that a modification to the original HP definition made to deal with an example by Hopkins and Pearl may not be necessary. By extending another definition, a sequence of models can be constructed, each one a conservative extension of the one before, where the question of whether X=x is cause of Y=y alternates between being true and false. |

11:45 | Axiomatizing Rationality SPEAKER: unknown ABSTRACT. We provide a sound and complete axiomatization for a class of logics appropriate for reasoning about the rationality of players in games. Essentially the same axiomatization applies to a wide class of decision rules. |

12:15 | âˆƒGUARANTEENASH for Boolean Games Is NEXP-Hard SPEAKER: unknown ABSTRACT. We show that determining whether a Boolean game has an equilibrium that guarantees every player a given payoff is NEXP-hard. In the proof we encode a non-deterministic, exponential time Turing machine by having one player play a mixed strategy from which we can infer a computation history of the machine, and the other players verify that this history does in fact represent a valid, accepting run. |