Above are papers related to the following paper: [Paper_ID] Paper #101263. 	[Title] Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries [Abstract] We study the problem of efficiently learning to play a game optimally against an unknown adversary chosen from a computationally bounded class. We both contribute to the line of research on playing games against finite automata, and expand the scope of this research by considering new classes of adversaries. We introduce the natural notions of games against recent history adversaries (whose current action is determined by some simple boolean formula on the recent history of play), and games against statistical adversaries (whose current action is determined by some simple function of the statistics of the entire history of play). In both cases we give efficient algorithms for learning to play penny-matching and a more difficult game called contract . We also give the most powerful positive result to date for learning to play against finite automata, an efficient algorithm for learning to play any game against any finite automata with probabilistic actions and low cover time. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1112099. 	[Title] The Power of a Pebble: Exploring and Mapping Directed Graphs [Abstract] Exploring and mapping an unknown environment is a fundamental problem, which is studied in a variety of contexts. Many works have focused on finding efficient solutions to restricted versions of the problem. In this paper, we consider a model that makes very limited assumptions on the environment and solve the mapping problem in this general setting. We model the environment by an unknown directed graph G, and consider the problem of a robot exploring and mapping G. We do not assume that the vertices of G are labeled, and thus the robot has no hope of succeeding unless it is given some means of distinguishing between vertices. For this reason we provide the robot with a pebble a device that it can place on a vertex and use to identify the vertex later. In this paper we show: (1) If the robot knows an upper bound on the number of vertices then it can learn the graph efficiently with only one pebble. (2) If the robot does not know an upper bound on the number of vertices n, then fi(log log n) pebbles are both necessary and sufficient. In both cases our algorithms are deterministic. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #12195. 	[Title] Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract) [Abstract] This paper describes new and efficient algorithms for learning deterministic finite automata. Our approach is primarily distinguished by two features: (1) the adoption of an average-case setting to model the "typical" labeling of a finite automaton, while retaining a worst-case model for the underlying graph of the automaton, along with (2) a learning model in which the learner is not provided with the means to experiment with the machine, but rather must learn solely by observing the automaton's output behavior on a random input sequence. The main contribution of this paper is in presenting the first efficient algorithms for learning non-trivial classes of automata in an entirely passive learning model. We adopt an on-line learning model in which the learner is asked to predict the output of the next state, given the next symbol of the random input sequence; the goal of the learner is to make as few prediction mistakes as possible. Assuming the learner has a means of resetting the target machine to a fixed start state, we first present an efficient algorithm that makes an expected polynomial number of mistakes in this model. Next, we show how this first algorithm can be used as a subroutine by a second algorithm that also makes a polynomial number of mistakes even in the absence of a reset. Along the way, we prove a number of combinatorial results for randomly labeled automata. We also show that the labeling of the states and the bits of the input sequence need not be truly random, but merely semi-random. Finally, we discuss an extension of our results to a model in which automata are used to represent distributions over binary strings. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #17476. 	[Title] Markov games as a framework for multi-agent reinforcement learning [Abstract] In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function. In this solipsistic view, secondary agents can only be part of the environment and are therefore fixed in their behavior. The framework of Markov games allows us to widen this view to include multiple adaptive agents with interacting or competing goals. This paper considers a step in this direction in which exactly two agents with diametrically opposed goals share an environment. It describes a Q-learning-like algorithm for finding optimal policies and demonstrates its application to a simple two-player game in which the optimal policy is probabilistic. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #152227. 	[Title] Learning DFA from Simple Examples [Abstract] We present a framework for learning DFA from simple examples. We show that efficient PAC learning of DFA is possible if the class of distributions is restricted to simple distributions where a teacher might choose examples based on the knowledge of the target concept. This answers an open research question posed in Pitt's seminal paper: Are DFA's PAC-identifiable if examples are drawn from the uniform distribution, or some other known simple distribution? Our approach uses the RPNI algorithm for learning DFA from labeled examples. In particular, we describe an efficient learning algorithm for exact learning of the target DFA with high probability when a bound on the number of states (N ) of the target DFA is known in advance. When N is not known, we show how this algorithm can be used for efficient PAC learning of DFAs. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #12165. 	[Title] The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs [Abstract] We show that two cooperating robots can learn exactly any strongly-connected directed graph with n indistinguishable nodes in expected time polynomial in n. We introduce a new type of homing sequence for two robots, which helps the robots recognize certain previously-seen nodes. We then present an algorithm in which the robots learn the graph and the homing sequence simultaneously by actively wandering through the graph. Unlike most previous learning results using homing sequences, our algorithm does not require a teacher to provide counterexamples. Furthermore, the algorithm can use efficiently any additional information available that distinguishes nodes. We also present an algorithm in which the robots learn by taking random walks. The rate at which a random walk on a graph converges to the stationary distribution is characterized by the conductance of the graph. Our random-walk algorithm learns in expected time polynomial in n and in the inverse of the conductance and is more efficient than the homing-sequence algorithm for high-conductance graphs. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28456. 	[Title] A Competitive Approach to Game Learning [Abstract] Machine learning of game strategies has often depended on competitive methods that continually develop new strategies capable of defeating previous ones. We use a very inclusive definition of game and consider a framework within which a competitive algorithm makes repeated use of a strategy learning component that can learn strategies which defeat a given set of opponents. We describe game learning in terms of sets H and X of first and second player strategies, and connect the model with more familiar models of concept learning. We show the importance of the ideas of teaching set [20] and specification number [19] k in this new context. The performance of several competitive algorithms is investigated, using both worst-case and randomized strategy learning algorithms. Our central result (Theorem 4) is a competitive algorithm that solves games in a total number of strategies polynomial in lg(jHj), lg(jX j), and k. Its use is demonstrated, including an application in concept learning with a new kind of counterexample oracle. We conclude with a complexity analysis of game learning, and list a number of new questions arising from this work. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #61069. 	[Title] Tracking the red queen: Measurements of adaptive progress in co-evolution ary simulations. In Third European [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #12238. 	[Title] Exactly Learning Automata with Small Cover Time [Abstract] We present algorithms for exactly learning unknown environments that can be described by deterministic finite automata. The learner performs a walk on the target automaton, where at each step it observes the output of the state it is at, and chooses a labeled edge to traverse to the next state. We assume that the learner has no means of a reset, and we also assume that the learner does not have access to a teacher that answers equivalence queries and gives the learner counterexamples to its hypotheses. We present two algorithms, one assumes that the outputs observed by the learner are always correct and the other assumes that the outputs might be erroneous. The running times of both algorithms are polynomial in the cover time of the underlying graph of the target automaton. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #101261. 	[Title] Optimality and Domination in Repeated Games with Bounded Players [Abstract] We examine questions of optimality and domination in repeated stage games where one or both players may draw their strategies only from (perhaps different) computationally bounded sets. We also consider optimality and domination when bounded convergence rates of the infinite payoff. We develop a notion of a "grace period" to handle the problem of vengeful strategies. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1107567. 	[Title] Learning Algorithms with Applications to Robot Navigation and Protein Folding [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1139928. 	[Title] Planning and Learning in an Adversarial Robotic Game [Abstract] 1 This paper demonstrates the tandem use of a finite automata learning algorithm and a utility planner for an adversarial robotic domain. For many applications, robot agents need to predict the movement of objects in the environment and plan to avoid them. When the robot has no reasoning model of the object, machine learning techniques can be used to generate one. In our project, we learn a DFA model of an adversarial robot and use the automaton to predict the next move of the adversary. The robot agent plans a path to avoid the adversary at the predicted location while fulfilling the goal requirements. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #887. 	[Title] Some studies in machine learning using the game of checkers. IBM Journal, 3(3):211-229, 1959. Some [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28504. 	[Title] TD Models: Modeling the World at a Mixture of Time Scales [Abstract] Temporal-difference (TD) learning can be used not just to predict rewards, as is commonly done in reinforcement learning, but also to predict states, i.e., to learn a model of the world's dynamics. We present theory and algorithms for intermixing TD models of the world at different levels of temporal abstraction within a single structure. Such multi-scale TD models can be used in model-based reinforcement-learning architectures and dynamic programming methods in place of conventional Markov models. This enables planning at higher and varied levels of abstraction, and, as such, may prove useful in formulating methods for hierarchical or multi-level planning and reinforcement learning. In this paper we treat only the prediction problem|that of learning a model and value function for the case of fixed agent behavior. Within this context, we establish the theoretical foundations of multi-scale models and derive TD algorithms for learning them. Two small computational experiments are presented to test and illustrate the theory. This work is an extension and generalization of the work of Singh (1992), Dayan (1993), and Sutton & Pinette (1985). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #57119. 	[Title] New Methods for Competitive Coevolution [Abstract] We consider "competitive coevolution," in which fitness is based on direct competition among individuals selected from two independently evolving populations of "hosts" and "parasites." Competitive coevolution can lead to an "arms race," in which the two populations reciprocally drive one another to increasing levels of performance and complexity. We use the games of Nim and 3-D Tic-Tac-Toe as test problems to explore three new techniques in competitive coevolution. "Competitive fitness sharing" changes the way fitness is measured, "shared sampling" provides a method for selecting a strong, diverse set of parasites, and the "hall of fame" encourages arms races by saving good individuals from prior generations. We provide several different motivations for these methods, and mathematical insights into their use. Experimental comparisons are done, and a detailed analysis of these experiments is presented in terms of testing issues, diversity, extinction, arms race progress measurements, and drift. Please summarise all the given information.

Paper #101263 has the title `Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries  
` and the abstract `We study the problem of efficiently learning to play a game optimally against an unknown adversary chosen from a computationally bounded class. We both contribute to the line of research on playing games against finite automata, and expand the scope of this research by considering new classes of adversaries. We introduce the natural notions of games against recent history adversaries (whose current action is determined by some simple boolean formula on the recent history of play), and games against statistical adversaries (whose current action is determined by some simple function of the statistics of the entire history of play). In both cases we give efficient algorithms for learning to play penny-matching and a more difficult game called contract . We also give the most powerful positive result to date for learning to play against finite automata, an efficient algorithm for learning to play any game against any finite automata with probabilistic actions and low cover time. 
`.
Question: Which category should Paper #101263 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #101263 has the title `Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries  
` and the abstract `We study the problem of efficiently learning to play a game optimally against an unknown adversary chosen from a computationally bounded class. We both contribute to the line of research on playing games against finite automata, and expand the scope of this research by considering new classes of adversaries. We introduce the natural notions of games against recent history adversaries (whose current action is determined by some simple boolean formula on the recent history of play), and games against statistical adversaries (whose current action is determined by some simple function of the statistics of the entire history of play). In both cases we give efficient algorithms for learning to play penny-matching and a more difficult game called contract . We also give the most powerful positive result to date for learning to play against finite automata, an efficient algorithm for learning to play any game against any finite automata with probabilistic actions and low cover time. 
`.
Question: Which category should Paper #101263 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Theory