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International audience; One task of game designers is to give NPCs fun behaviors, where "fun" can have many different manifestations. Several classic methods exist in Game AI to model NPCs' behaviors; one of them is utilitybased AI. Utility functions constitute a powerful tool to define behaviors but can be tedious and timeconsuming to make and tune correctly until the desired behavior is achieved. Here, we propose a method to learn utility functions from data collected after some human-played games, to recreate a target behavior. Utility functions are modeled using Interpretable Compositional Networks, allowing us to get interpretable results, unlike regular neural networks. We show our method can handle noisy data and learn utility functions able to credibly reproduce different target behaviors, with a median accuracy from 64.5% to 83.7%, using the FightingICE platform, an environment for AI agent competitions. We believe our method can be useful to game designers to quickly prototype NPCs' behaviors, and even to define their final utility functions.

Bisimilarity as an equivalence notion of systems has been central to process theory. Due to the recent rise of interest in quantitative systems (probabilistic, weighted, hybrid, etc.), bisimilarity has been extended in various ways: notably, bisimulation metric between probabilistic systems. An important feature of bisimilarity is its game-theoretic characterization, where Spoiler and Duplicator play against each other; extension of bisimilarity games to quantitative settings has been actively pursued too. In this paper, we present a general framework that uniformly describes game characterizations of bisimilarity-like notions. Our framework is formalized categorically using fibrations and coalgebras. In particular, our characterization of bisimilarity in terms of fibrational predicate transformers allows us to derive codensity bisimilarity games: a general categorical game characterization of bisimilarity. Our framework covers known bisimilarity-like notions (such as bisimulation metric) as well as new ones (including what we call bisimulation topology)., Comment: 13 pages + 3 page appendix, to appear in Proceedings of the 34th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS 2019)

This is the author version of the manuscript of the same name published in the proceedings of the 23rd International Conference on Engineering of Complex Computer Systems (ICECCS 2018).; International audience; Given a log and a specification, timed pattern matching aims at exhibiting for which start and end dates a specification holds on that log. For example, "a given action is always followed by another action before a given deadline". This problem has strong connections with monitoring real-time systems. We address here timed pattern matching in presence of an uncertain specification, i.e., that may contain timing parameters (e.g., the deadline can be uncertain or unknown). That is, we want to know for which start and end dates, and for what values of the deadline, this property holds. Or what is the minimum or maximum deadline (together with the corresponding start and end dates) for which this property holds. We propose here a framework for timed pattern matching based on parametric timed model checking. In contrast to most parametric timed problems, the solution is effectively computable, and we perform experiments using IMITATOR to show the applicability of our approach.