Towards Strategic Kriegspiel Play with Opponent Modeling

AAAI Conferences

Kriesgpiel, or partially observable chess, is appealing to the AI community due to its similarity to real-world applications in which a decision maker is not a lone agent changing the environment. This paper applies the framework of Interactive POMDPs to design a competent Kriegspiel player. The novel element, compared to the existing approaches, is to model the opponent as a competent player and to predict his likely moves. The moves of our own player can then be computed based on these predictions. The problem is challenging because, first, there are many possible world states the agent has to keep track of.

CAPIR: Collaborative Action Planning with Intention Recognition

AAAI Conferences

We apply decision theoretic techniques to construct non-player characters that are able to assist a human player in collaborative games. The method is based on solving Markov decision processes, which can be difficult when the game state is described by many variables. To scale to more complex games, the method allows decomposition of a game task into subtasks, each of which can be modelled by a Markov decision process. Intention recognition is used to infer the subtask that the human is currently performing, allowing the helper to assist the human in performing the correct task. Experiments show that the method can be effective, giving near-human level performance in helping a human in a collaborative game.

How a Bayesian Approaches Games Like Chess

AAAI Conferences

Eric B. Baum 1 NEC Research Institute, 4 Independence Way, Princeton NJ 08540 eric@research.NJ.NEC.COM Abstract The point of game tree search is to insulate oneself from errors in the evaluation function. The standard approach is to grow a full width tree as deep as time allows, and then value the tree as if the leaf evaluations were exact. This has been effective in many games because of the computational efficiency of the alpha-beta algorithm. A Bayesian would suggest instead to train a model of one's uncertainty. This model adds extra information in addition to the standard evaluation function. Within such a formal model, there is an optimal tree growth procedure and an optimal method of valueing the tree. We describe how to optimally value the tree, and how to approximate on line the optimal tree to search.

A Generalized Multidimensional Evaluation Framework for Player Goal Recognition

AAAI Conferences

Recent years have seen a growing interest in player modeling, which supports the creation of player-adaptive digital games. A central problem of player modeling is goal recognition, which aims to recognize players’ intentions from observable gameplay behaviors. Player goal recognition offers the promise of enabling games to dynamically adjust challenge levels, perform procedural content generation, and create believable NPC interactions. A growing body of work is investigating a wide range of machine learning-based goal recognition models. In this paper, we introduce GOALIE, a multidimensional framework for evaluating player goal recognition models. The framework integrates multiple metrics for player goal recognition models, including two novel metrics, n-early convergence rate and standardized convergence point . We demonstrate the application of the GOALIE framework with the evaluation of several player goal recognition models, including Markov logic network-based, deep feedforward neural network-based, and long short-term memory network-based goal recognizers on two different educational games. The results suggest that GOALIE effectively captures goal recognition behaviors that are key to next-generation player modeling.

Efficient Bayesian Inference for Generalized Bradley-Terry Models Machine Learning

The Bradley-Terry model is a popular approach to describe probabilities of the possible outcomes when elements of a set are repeatedly compared with one another in pairs. It has found many applications including animal behaviour, chess ranking and multiclass classification. Numerous extensions of the basic model have also been proposed in the literature including models with ties, multiple comparisons, group comparisons and random graphs. From a computational point of view, Hunter (2004) has proposed efficient iterative MM (minorization-maximization) algorithms to perform maximum likelihood estimation for these generalized Bradley-Terry models whereas Bayesian inference is typically performed using MCMC (Markov chain Monte Carlo) algorithms based on tailored Metropolis-Hastings (M-H) proposals. We show here that these MM\ algorithms can be reinterpreted as special instances of Expectation-Maximization (EM) algorithms associated to suitable sets of latent variables and propose some original extensions. These latent variables allow us to derive simple Gibbs samplers for Bayesian inference. We demonstrate experimentally the efficiency of these algorithms on a variety of applications.