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Cognitive Radio: BrainEmpowered Wireless Communications
, 2005
"... Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a softwaredefined radio, is defined as an intelligent wireless communication system that is aware of its environment and use ..."
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Cited by 1541 (4 self)
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Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a softwaredefined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understandingbybuilding to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: • highly reliable communication whenever and wherever needed; • efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radioscene analysis. 2) Channelstate estimation and predictive modeling. 3) Transmitpower control and dynamic spectrum management. This paper also discusses the emergent behavior of cognitive radio.
Features of similarity.
 Psychological Review
, 1977
"... Similarity plays a fundamental role in theories of knowledge and behavior. It serves as an organizing principle by which individuals classify objects, form concepts, and make generalizations. Indeed, the concept of similarity is ubiquitous in psychological theory. It underlies the accounts of stimu ..."
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Cited by 1455 (2 self)
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Similarity plays a fundamental role in theories of knowledge and behavior. It serves as an organizing principle by which individuals classify objects, form concepts, and make generalizations. Indeed, the concept of similarity is ubiquitous in psychological theory. It underlies the accounts of stimulus and response generalization in learning, it is employed to explain errors in memory and pattern recognition, and it is central to the analysis of connotative meaning. Similarity or dissimilarity data appear in di¤erent forms: ratings of pairs, sorting of objects, communality between associations, errors of substitution, and correlation between occurrences. Analyses of these data attempt to explain the observed similarity relations and to capture the underlying structure of the objects under study. The theoretical analysis of similarity relations has been dominated by geometric models. These models represent objects as points in some coordinate space such that the observed dissimilarities between objects correspond to the metric distances between the respective points. Practically all analyses of proximity data have been metric in nature, although some (e.g., hierarchical clustering) yield treelike structures rather than dimensionally organized spaces. However, most theoretical and empirical analyses of similarity assume that objects can be adequately represented as points in some coordinate space and that dissimilarity behaves like a metric distance function. Both dimensional and metric assumptions are open to question. It has been argued by many authors that dimensional representations are appropriate for certain stimuli (e.g., colors, tones) but not for others. It seems more appropriate to represent faces, countries, or personalities in terms of many qualitative features than in terms of a few quantitative dimensions. The assessment of similarity between such stimuli, therefore, may be better described as a comparison of features rather than as the computation of metric distance between points. A metric distance function, d, is a scale that assigns to every pair of points a nonnegative number, called their distance, in accord with the following three axioms: Minimality: dða; bÞ b dða; aÞ ¼ 0: Symmetry: dða; bÞ ¼ dðb; aÞ: The triangle inequality: dða; bÞ þ dðb; cÞ b dða; cÞ: To evaluate the adequacy of the geometric approach, let us examine the validity of the metric axioms when d is regarded as a measure of dissimilarity. The minimality axiom implies that the similarity between an object and itself is the same for all
Markov games as a framework for multiagent reinforcement learning
 IN PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING
, 1994
"... 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 ..."
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Cited by 601 (13 self)
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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 Qlearninglike algorithm for finding optimal policies and demonstrates its application to a simple twoplayer game in which the optimal policy is probabilistic.
The complexity of computing a Nash equilibrium
, 2006
"... We resolve the question of the complexity of Nash equilibrium by showing that the problem of computing a Nash equilibrium in a game with 4 or more players is complete for the complexity class PPAD. Our proof uses ideas from the recentlyestablished equivalence between polynomialtime solvability of n ..."
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Cited by 329 (23 self)
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We resolve the question of the complexity of Nash equilibrium by showing that the problem of computing a Nash equilibrium in a game with 4 or more players is complete for the complexity class PPAD. Our proof uses ideas from the recentlyestablished equivalence between polynomialtime solvability of normalform games and graphical games, and shows that these kinds of games can implement arbitrary members of a PPADcomplete class of Brouwer functions. 1
Tussle in cyberspace: Defining tomorrow’s Internet
 In Proc. ACM SIGCOMM
, 2002
"... Abstract—The architecture of the Internet is based on a number of principles, including the selfdescribing datagram packet, the endtoend arguments, diversity in technology and global addressing. As the Internet has moved from a research curiosity to a recognized component of mainstream society, n ..."
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Cited by 307 (10 self)
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Abstract—The architecture of the Internet is based on a number of principles, including the selfdescribing datagram packet, the endtoend arguments, diversity in technology and global addressing. As the Internet has moved from a research curiosity to a recognized component of mainstream society, new requirements have emerged that suggest new design principles, and perhaps suggest that we revisit some old ones. This paper explores one important reality that surrounds the Internet today: different stakeholders that are part of the Internet milieu have interests that may be adverse to each other, and these parties each vie to favor their particular interests. We call this process “the tussle.” Our position is that accommodating this tussle is crucial to the evolution of the network’s technical architecture. We discuss some examples of tussle, and offer some technical design principles that take it into account. Index Terms—Competition, design principles, economics, network architecture, trust, tussle. I.
On the Length of Programs for Computing Finite Binary Sequences
 Journal of the ACM
, 1966
"... The use of Turing machines for calculating finite binary sequences is studied from the point of view of information theory and the theory of recursive functions. Various results are obtained concerning the number of instructions in programs. A modified form of Turing machine is studied from the same ..."
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Cited by 295 (8 self)
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The use of Turing machines for calculating finite binary sequences is studied from the point of view of information theory and the theory of recursive functions. Various results are obtained concerning the number of instructions in programs. A modified form of Turing machine is studied from the same point of view. An application to the problem of defining a patternless sequence is proposed in terms of the concepts here 2 G. J. Chaitin developed. Introduction In this paper the Turing machine is regarded as a general purpose computer and some practical questions are asked about programming it. Given an arbitrary finite binary sequence, what is the length of the shortest program for calculating it? What are the properties of those binary sequences of a given length which require the longest programs? Do most of the binary sequences of a given length require programs of about the same length? The questions posed above are answered in Part 1. In the course of answering them, the logical ...
Decision field theory: A dynamiccognitive approach to decision making (Tech
, 1989
"... Decision field theory provides for a mathematical foundation leading to a dynamic, stochastic theory of decision behavior in an uncertain environment. This theory is used to explain (a) violations of stochastic dominance, (b) violations of strong stochastic transitivity, (c) violations of independ ..."
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Cited by 264 (14 self)
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Decision field theory provides for a mathematical foundation leading to a dynamic, stochastic theory of decision behavior in an uncertain environment. This theory is used to explain (a) violations of stochastic dominance, (b) violations of strong stochastic transitivity, (c) violations of independence between alternatives, (d) serial position effects on preference, (e) speedaccuracy tradeoff effects in decision making, (f) the inverse relation between choice probability and decision time, (g) changes in the direction of preference under time pressure, (h) slower decision times for avoidance as compared with approach conflicts, and (i) preference reversals between choice and selling price measures of preference. The proposed theory is compared with 4 other theories of decision making under uncertainty. Beginning with von Neumann and Morgenstern's (1947) classic expected utility theory, steady progress has been made in the development of formal theories of decision making under risk and uncertainty. For rational theorists, the goal has been to formulate a logical foundation for representing the preferences of an ideal decision maker (e.g., Machina, 1982; Savage,
Emotionbased choice
 Journal of Experimental Psychology: General
, 1999
"... In this article the authors develop a descriptive theory of choice using anticipated emotions. People are assumed to anticipate how they will feel about the outcomes of decisions and use their predictions to guide choice. The authors measure the pleasure associated with monetary outcomes of gambles ..."
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Cited by 226 (7 self)
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In this article the authors develop a descriptive theory of choice using anticipated emotions. People are assumed to anticipate how they will feel about the outcomes of decisions and use their predictions to guide choice. The authors measure the pleasure associated with monetary outcomes of gambles and offer an account of judged pleasure called decision affect theory. Then they propose a theory of choices between gambles based on anticipated pleasure. People are assumed to choose the option with greater subjective expected pleasure. Similarities and differences between subjective expected pleasure theory and subjective expected utility theory are discussed. Emotions have powerful effects on choice. Our actual feelings of happiness, sadness, and anger both color and shape our decisions. In addition, our imagined feelings of guilt, elation, or regret influence our decisions. In this article we refer to these two influences as experienced emotions and anticipated emotions. Experienced emotions affect many levels of cognitive processing. When we are in good moods, we are better problem solvers (Isen, 1984, 1987, 1993), more likely to remember happy events (Bower, 1981), more risk seeking (Kahn & Isen, 1993), and more optimistic about the chances of favorable events (Wright & Bower, 1992; Nygren, Isen, Taylor, & Dulin, 1996). When we are in bad moods, we are more likely to recall negative events (Bower, 1981) and overestimate the chances of unfavorable events (Johnson & Tversky, 1983). If we are also aroused, we make less discriminate use of information