On measuring the usefulness of modeling in a competitive and cooperative environment

AAAI Conferences

Leonardo Garrido Ram6n Brena Centro de Inteligencia Artificial, Tecnol6gico de Monterrey Abstract This paper presents recent results of our experimental work in quantifying exactly how useful is building models about other agents using no more than the observation of others' behavior. The testbed we used in our experiments is an abstraction of the meeting scheduling problem, called the Meeting Scheduling Game, which has competitive as well as cooperative features. The agents are selfish, and use a rational decision theoretic approach based on the probabilistic models that the agent is learning. We view agent modeling as an iterative and gradual process, where every new piece of information about a particular agent is analyzed in such a way that the model of the agent is further refined. We propose a framework for measuring the performance of different modelling strategies and establish quantified lower and upper limits for the performance of any modeling strategy. Finally, we contrast the performances of a modeler from an individual and from a collective point of view, comparing the benefits for the modeler itself as well as for the group as a whole. Introduction Katia Sycara The Robotics Institute, Carnegie Mellon University Several approaches in the field of multiagent systems (MAS) (Durfee 1991; Wooldridge & Jennings 1995) make heavy use of beliefs as an internal model of the world (Bratman 1987) One form of belief of particular importance in multiagent systems are the agent's beliefs about other agents (Vidal & Durfee 1997b). This kind of belief could come from preexisting knowledge base (a kind of"prejudice"), or could be inferred from observing others' behavior. The purpuse of a modelling activity could be to benefit a specific agent, in the case of "selfish" agents, or to improve the performance of a group as a whole, in the case of cooperative agents -or even a combination of both.


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.


Learning Latent Block Structure in Weighted Networks

arXiv.org Machine Learning

Community detection is an important task in network analysis, in which we aim to learn a network partition that groups together vertices with similar community-level connectivity patterns. By finding such groups of vertices with similar structural roles, we extract a compact representation of the network's large-scale structure, which can facilitate its scientific interpretation and the prediction of unknown or future interactions. Popular approaches, including the stochastic block model, assume edges are unweighted, which limits their utility by throwing away potentially useful information. We introduce the `weighted stochastic block model' (WSBM), which generalizes the stochastic block model to networks with edge weights drawn from any exponential family distribution. This model learns from both the presence and weight of edges, allowing it to discover structure that would otherwise be hidden when weights are discarded or thresholded. We describe a Bayesian variational algorithm for efficiently approximating this model's posterior distribution over latent block structures. We then evaluate the WSBM's performance on both edge-existence and edge-weight prediction tasks for a set of real-world weighted networks. In all cases, the WSBM performs as well or better than the best alternatives on these tasks.


Sequential effects reflect parallel learning of multiple environmental regularities

Neural Information Processing Systems

Across a wide range of cognitive tasks, recent experience influences behavior. For example, when individuals repeatedly perform a simple two-alternative forced-choice task (2AFC), response latencies vary dramatically based on the immediately preceding trial sequence. These sequential effects have been interpreted as adaptation to the statistical structure of an uncertain, changing environment (e.g. Jones & Sieck, 2003; Mozer, Kinoshita, & Shettel, 2007; Yu & Cohen, 2008). The Dynamic Belief Model (DBM) (Yu & Cohen, 2008) explains sequential effects in 2AFC tasks as a rational consequence of a dynamic internal representation that tracks second-order statistics of the trial sequence (repetition rates) and predicts whether the upcoming trial will be a repetition or an alternation of the previous trial. Experimental results suggest that first-order statistics (base rates) also influence sequential effects. We propose a model that learns both first- and second-order sequence properties, each according to the basic principles of the DBM but under a unified inferential framework. This model, the Dynamic Belief Mixture Model (DBM2), obtains precise, parsimonious fits to data. Furthermore, the model predicts dissociations in behavioral (Maloney, Dal Martello, Sahm, & Spillmann, 2005) and electrophysiological studies (Jentzsch & Sommer, 2002), supporting the psychological and neurobiological reality of its two components.


Applying Discrete PCA in Data Analysis

arXiv.org Machine Learning

Methods for analysis of principal components in discrete data have existed for some time under various names such as grade of membership modelling, probabilistic latent semantic analysis, and genotype inference with admixture. In this paper we explore a number of extensions to the common theory, and present some application of these methods to some common statistical tasks. We show that these methods can be interpreted as a discrete version of ICA. We develop a hierarchical version yielding components at different levels of detail, and additional techniques for Gibbs sampling. We compare the algorithms on a text prediction task using support vector machines, and to information retrieval.