We present data and argument to show that in Tetris-a real-time, interactive video game-certain cognitive and perceptual problems ore more quicktv, easily, and reliably solved by performing actions in the world than by performing com-putational actions in the head atone. We have found that some of the translations and rotations made by players of this video game are best understood as actions that use the world to improve cognition. These actions are not used to implement a plan, or to implement a reaction; they are used to change the world in order to simplify the problem-solving task. Thus, we distinguish pragmatic octions--actions performed to bring one physically closer to a goal-from epistemic actions-actions performed to uncover informatioan that is hidden or hard to compute mentally. To illustrate the need for epistemic actions, we first develop a standard information-processing model of Tetris cognition and show that it cannot explain performance data from human players of the game-even when we relax the assumption of fully sequential processing. Standard models disregard many actions taken by players because they appear unmotivated or superfluous. How-ever, we show that such actions are actually far from superfluous; they play a valuable role in improving human performance. We argue that traditional accounts are limited because they regard action as having o single function: to change the world. By recognizing a second function of action-an epistemic func-tion-we can explain many of the actions that a traditional model cannot. Al-though our argument is supported by numerous examples specifically from Tetris, we outline how the new category of epistemic action can be incorporated into theories of action more generally. In this article, we introduce the general idea of an epistemic action and discuss its role in Tetris, a real-time, interactive video game. Epistemic actions-physical actions that make mental computation easier, faster, or more We thank Steve Haehnichen for his work on the initial implementations of Tetris and

We are interested in the problem faced byanagent with limited computational capabilities, embedded in a complex environment with other agents and processes not under its control. Careful management of computational resources is important for complex problem-solving tasks in which the time spent in decision making affects the quality of the responses generated by a system.

e case of location and scale parameters, rate constants, and in Bernoulli trials with unknown probability of success. In realistic problems, both the transformation group analysis and the principle of maximum entropy are needed to determine the prior. The distributions thus found are uniquely determined by the prior information, independently of the choice of parameters. In a certain class of problems, therefore, the prior distributions may now be claimed to be fully as "objective" as the sampling distributions. I. Background of the problem Since the time of Laplace, applications of probability theory have been hampered by difficulties in the treatment of prior information. In realistic problems of decision or inference, we often have prior information which is highly relevant to the question being asked; to fail to take it into account is to commit the most obvious inconsistency of reasoning and may lead to absurd or dangerously misleading results. As an extreme examp

We present a formalization of the Recursive Modeling Method, which we have previously, somewhat informally, proposed as a method that autonomous artificial agents can use for intelligent coordination and communication with other agents. Our formalism is closely related to models proposed in the area of game theory, but contains new elements that lead to a different solution concept. The advantage of our solution method is that always yields the optimal solution, which is the rational action of the agent in a multi-agent environment, given the agent's state of knowledge and its preferences, and that it works in realistic cases when agents have only a finite amount of information about the agents they interact with. Introduction Since its initial conceptual development several years ago (Gmytrasiewicz, Durfee, & Wehe 1991a; 1991b), the Recursive Modeling Method (RMM) has provided a powerful decision-theoretic underpinning for coordination and communication decisionmaking, including dec...

We address the issue of rational communicative behavior among autonomous intelligent agents that have to make decisions as to what, to whom, and how to communicate. We treat communicative actions as aimed at increasing the efficiency of interaction among agents. We postulate that a rational speaker design a speech act so as to maximally increase the benefit obtained as the result of the interaction. We quantify the gain in the quality of interaction as the expected utility, and we present a framework that allows an agent to compute the expected utility of various communicative actions. Our framework uses the Recursive Modeling Method as the representation of the agent's state of knowledge, including the agent's preferences, abilities and beliefs about the world, as well as the beliefs the agent has about the other agents, the beliefs it has about the other agents ' beliefs, and so on. A decision-theoretic pragmatics of a communicative act can be then defined as the transformation it induces on the agent's state of knowledge about its decision-making situation. This transformation leads to a change in the quality of the interaction, expressed in terms of the benefit to the agent. We analyze decision-theoretic pragmatics of a number of important communicative acts, and investigate their expected utility using examples.

We adopt the decision-theoretic principle of expected utility maximization as a paradigm for designing autonomous rational agents, and present a framework that uses this paradigm to determine the choice of coordinated action. We endow an agent with a specialized representation that captures the agent's knowledge about the environment and about the other agents, including its knowledge about their states of knowledge, which can include what they know about the other agents, and so on. This reciprocity leads to a recursive nesting of models. Our framework puts forth a representation for the recursive models and, under the assumption that the nesting of models is finite, uses dynamic programming to solve this representation for the agent's rational choice of action. Using a decision-theoretic approach, our work addresses concerns of agent decision-making about coordinated action in unpredictable situations, without imposing upon agents pre-designed prescriptions, or protocols, ...

. Drawing on our work in the area of distributed artificial intelligence, we put forth a framework for modeling a human user interacting with a knowledge-based system. We assume that the human user is situated in some decision making setting, and view the computer system as taking an active role in supporting the user's decision making and problem solving activities. The model the system has of the decision making situation and of the user can be applied to determine what the system should do, both in terms of the system's physical action, if such is possible, as well as in terms of the information that should be transmitted to the user. An important part of the user's model is the model that the user may have of the system itself, and, further, how the user may think it is being modeled by the system. Our framework, the Recursive Modeling Method (RMM), explicitly represents this nesting of models, and lets the system to coordinate with the expected actions of the human user, and to r...

We present the design, implementation and evaluation of a location detection system built over a Radio Frequency network based on the IEEE 802.11 standard. Our system employs beacons to broadcast identifying packets from strategic positions within a building infrastructure in such a way that each resolvable position is covered by a unique collection of beacons; a user of such a system can thus determine his location by means of the beacon packets received. The locations from which beacons broadcast is determined from a formalization of the problem based on identifying codes over arbitrary graphs. We present experimental evidence that our location detecting system is practical and useful, and that it can achieve good accuracy even with a very small number of beacons. I.

This paper describes an algorithm for orthogonal clustering. That is, it nds multiple partitions of a domain. The Minimum Description Length (MDL) Principle is used to de ne a parameter-free evaluation function over all possible sets of partitions. In contrast, conventional clustering algorithms can only nd a single partition of a set of data. While they can be applied iteratively to create hierarchies, these are limited to tree structures. Orthogonal clustering, on the other hand, cannot form hierarchies deeper than one layer. Ideally one would want an algorithm which doesboth. However there are important problems for which orthogonal clustering is desirable. In particular, orthogonal clusters correspond to feature vectors, which are widely used throughout cognitive science. Hopefully, orthogonal clusters will also be useful for nding analogies. A side e ect which deserves more exploration is the induction of domain axioms in which the features

terns inside available data, by using specific statistical techniques [2]. Even if they are around from almost 50 years, pattern recognition approaches have recently gained a new popularity, due to emerging applications which are not only challanging, but also computationally expensive and very demanding like data mining (identifying a pattern or a correlation among data or an outlier in millions of multidimensional patterns), document classification (searching text documents), forecasting, multimedia organization and retrieval in databases, flexible information retrieval (product retrieval in e-commerce applications, solution retrieval in help-desk support), etc. . . The statistical approach to pattern recognition represents a pattern as a set of d features or attributes, by viewing it as a d-dimensional feature vector. Classical concepts from statistical decision theory [13] are then used to establish decision boundaries among pattern classes. The recognition system operates in two