IMP-minimax is the analog to minimax for games with imperfect information, like card games such as bridge or poker. It computes an optimal strategy for the game if the game has a single player and a certain natural property called perfect recall. IMP-minimax is described fully in a companion paper in this proceedings. Here we introduce an algorithm IMP-alpha-beta that is to IMP-minimax as alpha...

IX Chapter 0ABSTRACT The process of designing high-quality software systems is one of the major issues in software engineering research. Over the years, this has resulted in numerous design methods, each with specific qualities and drawbacks. For example, the Rational Unified Process is a comprehensive design process, which is proposed to support the major phases in the software engineering lif...

We consider imperfect information stochastic games where we require the players to use pure (i.e. non randomised) strategies. We consider reachability, safety, Büchi and coBüchi objectives, and investigate the existence of almost-sure/positively winning strategies for the first player when the second player is perfectly informed or more informed than the first player. We obtain decidability res...

A temporal question answering system must be able to deduce which qualitative temporal relation holds between two events, a reasoning task that is complicated by the fact that historical events tend to have a gradual beginning and ending. In this paper, we introduce an algebra of temporal relations that is well–suited to represent the qualitative temporal information we have at our disposal. We...

In minimax game-tree search, the min part of the minimax backup rule derives from what we will call the paranoid assumption: the assumption that the opponent will always choose a move that minimizes our payoff and maximizes his/her payoff (or our estimate of the payoff, if we cut off the search before reaching the end of the game). A potential criticism of this assumption is that the opponent m...

We study a model of games that combines concurrency, imperfect information and stochastic aspects. Those are finite states games in which, at each round, the two players choose, simultaneously and independently, an action. Then a successor state is chosen accordingly to some fixed probability distribution depending on the previous state and on the pair of actions chosen by the players. Imperfec...

Counterfactual Regret Minimization and variants (e.g. Public Chance Sampling CFR and Pure CFR) have been known as the best approaches for creating approximate Nash equilibrium solutions for imperfect information games such as poker. This paper introduces CFR, a new algorithm that typically outperforms the previously known algorithms by an order of magnitude or more in terms of computation time ...

One of the most challenging tasks when creating an adaptation mechanism is to transform domain knowledge into an evaluation function that adequately measures the quality of the generated solutions. The high complexity of modern video games makes the task to generate a suitable evaluation function for adaptive game AI even more difficult. Still, our aim is to fully automatically generate an eval...

In data-driven inverse optimization an observer aims to learn the preferences of an agent who solves a parametric optimization problem depending on an exogenous signal. Thus, the observer seeks the agent’s objective function that best explains a historical sequence of signals and corresponding optimal actions. We focus here on situations where the observer has imperfect information, that is, wh...