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46
Cognitive networks
 in Proc. of IEEE DySPAN 2005
, 2005
"... Abstract — This paper presents a definition and framework for a novel type of adaptive data network: the cognitive network. In a cognitive network, the collection of elements that make up the network observes network conditions and then, using prior knowledge gained from previous interactions with t ..."
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Cited by 1096 (7 self)
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Abstract — This paper presents a definition and framework for a novel type of adaptive data network: the cognitive network. In a cognitive network, the collection of elements that make up the network observes network conditions and then, using prior knowledge gained from previous interactions with the network, plans, decides and acts on this information. Cognitive networks are different from other “intelligent ” communication technologies because these actions are taken with respect to the endtoend goals of a data flow. In addition to the cognitive aspects of the network, a specification language is needed to translate the user’s endtoend goals into a form understandable by the cognitive process. The cognitive network also depends on a Software Adaptable Network that has both an external interface accessible to the cognitive network and network status sensors. These devices are used to provide control and feedback. The paper concludes by presenting a simple case study to illustrate a cognitive network and its framework. I.
Learning to Cooperate via Policy Search
, 2000
"... Cooperative games are those in which both agents share the same payoff structure. Valuebased reinforcementlearning algorithms, such as variants of Qlearning, have been applied to learning cooperative games, but they only apply when the game state is completely observable to both agents. Poli ..."
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Cited by 141 (4 self)
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Cooperative games are those in which both agents share the same payoff structure. Valuebased reinforcementlearning algorithms, such as variants of Qlearning, have been applied to learning cooperative games, but they only apply when the game state is completely observable to both agents. Policy search methods are a reasonable alternative to valuebased methods for partially observable environments. In this paper, we provide a gradientbased distributed policysearch method for cooperative games and compare the notion of local optimum to that of Nash equilibrium. We demonstrate the effectiveness of this method experimentally in a small, partially observable simulated soccer domain. 1 INTRODUCTION The interaction of decision makers who share an environment is traditionally studied in game theory and economics. The game theoretic formalism is very general, and analyzes the problem in terms of solution concepts such as Nash equilibrium [12], but usually works under the assu...
Learning a Local Similarity Metric for CaseBased Reasoning
 In International Conference on CaseBased Reasoning (ICCBR95
, 1995
"... . This paper presents a new class of local similarity metrics, called AASM, that are not symmetric and that can be adopted as the basic retrieval method in a CBR system. An anytime learning procedure is also introduced that, starting from an initial set of stored cases, improves the retrieval accura ..."
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Cited by 32 (6 self)
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. This paper presents a new class of local similarity metrics, called AASM, that are not symmetric and that can be adopted as the basic retrieval method in a CBR system. An anytime learning procedure is also introduced that, starting from an initial set of stored cases, improves the retrieval accuracy by modifying the local definition of the metric. The learning procedure is a reinforcement learning algorithm and can be run as a black box since no particular setting is required. With the aid of classical test sets it is shown that AASM can improve in many cases the accuracy of both nearest neighbour methods and Salzberg's NGE. Moreover, AASM can achieve significant data compression (10%) while maintainig the same accuracy as NN. 1 Introduction Classification methods based on nearest neighbor (NN) have many advantages compared with other classification techniques. First of all, NN supports incremental learning from new cases without degradation in performance on previous training data....
Reinforcement Learning by Policy Search
, 2000
"... One objective of artificial intelligence is to model the behavior of an intelligent agent interacting with its environment. The environment's transformations could be modeled as a Markov chain, whose state is partially observable to the agent and affected by its actions; such processes are know ..."
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Cited by 31 (2 self)
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One objective of artificial intelligence is to model the behavior of an intelligent agent interacting with its environment. The environment's transformations could be modeled as a Markov chain, whose state is partially observable to the agent and affected by its actions; such processes are known as partially observable Markov decision processes (POMDPs). While the environment's dynamics are assumed to obey certain rules, the agent does not know them and must learn. In this dissertation we focus on the agent's adaptation as captured by the reinforcement learning framework. Reinforcement learning means learning a policya mapping of observations into actionsbased on feedback from the environment. The learning can be viewed as browsing a set of policies while evaluating them by trial through interaction with the environment. The set of policies being searched is constrained by the architecture of the agent's controller. POMDPs require a controller to have a memory. We investigate various architectures for controllers with memory, including controllers with external memory, finite state controllers and distributed controllers for multiagent system. For these various controllers we work out the details of the algorithms which learn by ascending the gradient of expected cumulative reinforcement. Building on statistical learning theory and experiment design theory, a policy evaluation algorithm is developed for the case of experience reuse. We address the question of sufficient experience for uniform convergence of policy evaluation and obtain sample complexity bounds for various estimators. Finally, we demonstrate the performance of the proposed algorithms on several domains, the most complex of which is simulated adaptive packet routing in a telecommunication network.
Learning automatabased algorithms for finding minimum weakly connected dominating set in stochastic graphs
 INT. J. UNCERTAIN. FUZZINESS KNOWL.BASED SYST
, 2010
"... A weakly connected dominating set (WCDS) of graph G is a subset of G so that the vertex set of the given subset and all vertices with at least one endpoint in the subset induce a connected subgraph of G. The minimum WCDS (MWCDS) problem is known to be NPhard, and several approximation algorithms h ..."
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Cited by 10 (6 self)
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A weakly connected dominating set (WCDS) of graph G is a subset of G so that the vertex set of the given subset and all vertices with at least one endpoint in the subset induce a connected subgraph of G. The minimum WCDS (MWCDS) problem is known to be NPhard, and several approximation algorithms have been proposed for solving MWCDS in deterministic graphs. However, to the best of our knowledge no work has been done on finding the WCDS in stochastic graphs. In this paper, a definition of the MWCDS problem in a stochastic graph is first presented and then several learning automatabased algorithms are proposed for solving the stochastic MWCDS problem where the probability distribution function of the weight associated with the graph vertices is unknown. The proposed algorithms significantly reduce the number of samples needs to be taken from the vertices of the stochastic graph. It is shown that by a proper choice of the parameters of the proposed algorithms, the probability of finding the MWCDS is as close to unity as possible. Experimental results show the major superiority of the proposed algorithms over the standard sampling method in terms of the sampling rate.
Simulation Study of Multiple Intelligent Vehicle Control Using Stochastic Learning Automata
 IEEE Transactions on Systems, Man and Cybernetics  Part A : Systems and Humans
, 1997
"... An intelligent controller is described for an automated vehicle planning its trajectory based on sensor and communication data received. The intelligent controller is designed using a stochastic learning automaton. Using the data received from onboard sensors, two automata (for lateral and longitud ..."
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Cited by 10 (0 self)
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An intelligent controller is described for an automated vehicle planning its trajectory based on sensor and communication data received. The intelligent controller is designed using a stochastic learning automaton. Using the data received from onboard sensors, two automata (for lateral and longitudinal actions) are capable of learning the best possible actions to avoid collisions. The system has the advantage of being able to work in unmodeled stochastic environments. Computer simulation is a way to test the effectiveness of the learning automata method because the system becomes highly complex because of the presence of a large number of vehicles. Simulations for simultaneous lateral and longitudinal control of a vehicle using this method provide encouraging results. Multiple vehicle simulations are also given, and the resulting complexity is discussed. The analysis of the situations is made possible by the study of the interacting rewardpenalty mechanisms in individual vehicles. Simple scenarios consisting of multiple vehicles are defined as collections of discrete states, and each state is treated as a game of automata. The definition of the physical environment as a series of discrete state transitions associated with a "stationary automata environment" is the key to this analysis and to the design of the intelligent controller. The aim is to obtain the necessary and sufficient rules for state transitions to reach the goal state.
Internal and External Forces in Language Change
, 2000
"... If every productive form of linguistic expression can be described by some idealized human grammar, an individuals's variable linguistic behavior (Weinreich, Labov, & Herzog, 1968) can be modeled as a statistical distribution of multiple idealized grammars. The distribution of grammars is d ..."
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Cited by 9 (1 self)
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If every productive form of linguistic expression can be described by some idealized human grammar, an individuals's variable linguistic behavior (Weinreich, Labov, & Herzog, 1968) can be modeled as a statistical distribution of multiple idealized grammars. The distribution of grammars is determined by the interaction between the biological constraints on human grammar and the properties of linguistic data in the environment during the course of language acquisition. Such interaction can be formalized precisely and quantitatively in a mathematical model of language learning. Consequently, we model language change as the change in grammar distribution over time, which can be related to the statistical properties of historical linguistic data. As an empirical test, we apply the proposed model to explain the loss of the verbsecond phenomenon in Old French and Old English based on corpus studies of historical texts.
A Selectionist Theory of Language Acquisition
, 1999
"... This paper argues that developmental patterns in child language be taken seriously in computational models of language acquisition, and proposes a forreal theory that meets this criterion. We first present developmental facts that are problematic for statistical learning approaches which assume no p ..."
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Cited by 6 (1 self)
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This paper argues that developmental patterns in child language be taken seriously in computational models of language acquisition, and proposes a forreal theory that meets this criterion. We first present developmental facts that are problematic for statistical learning approaches which assume no prior knowledge of grammar, and for traditional learnability models which assume the learner moves from one UGdefined grammar to another. In contrast, we view language acquisition as a population of grammars associated with 'weights", that compete in a Darwinian selectionist process. Selection is made possible by the variationa! properties of individual grammars; specifically, their differential compatibility with the primary linguistic data in the environment. In addition to a convergence proof, we present empirical evidence in child language development, that a learner is best modeled as multiple grammars in coexistence and competition.
A Cellular Learning Automatabased Deployment Strategy for Mobile Wireless Sensor Networks
"... Abstract: One important problem which may arise in designing a deployment strategy for a wireless sensor network is how to deploy a specific number of sensor nodes throughout an unknown network area so that the covered section of the area is maximized. In a mobile sensor network, this problem can be ..."
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Cited by 6 (4 self)
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Abstract: One important problem which may arise in designing a deployment strategy for a wireless sensor network is how to deploy a specific number of sensor nodes throughout an unknown network area so that the covered section of the area is maximized. In a mobile sensor network, this problem can be addressed by first deploying sensor nodes randomly in some initial positions within the area of the network, and then letting sensor nodes to move around and find their best positions according to the positions of their neighboring nodes. The problem becomes more complicated if sensor nodes have no information about their positions or even their relative distances to each other. In this paper, we propose a cellular learning automatabased deployment strategy which guides the movements of sensor nodes within the area of the network without any sensor to know its position or its relative distance to other sensors. In the proposed algorithm, the learning automaton in each node in cooperation with the learning automata in the neighboring nodes controls the movements of the node in order to attain high coverage. Experimental results have shown that in noise free environments, the proposed algorithm can compete with the existing algorithms such as PF, DSSA, IDCA, and VEC in terms of network coverage. It has also been shown that in noisy environments, where utilized location estimation techniques such as GPSbased devices and localization algorithms experience inaccuracies in their measurements, or the movements of sensor nodes are not perfect and follow a probabilistic motion model, the proposed algorithm outperforms the existing algorithms in terms of network coverage.
Dynamic Algorithms for the Shortest Path Routing Problem: Learning AutomataBased Solutions
 IEEE Transactions on Systems, Man, and Cybernetics
, 2005
"... Abstract—This paper presents the first Learning Automatonbased solution to the dynamic single source shortest path problem. It involves finding the shortest path in a singlesource stochastic graph topology where there are continuous probabilistic updates in the edgeweights. The algorithm is signi ..."
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Cited by 4 (0 self)
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Abstract—This paper presents the first Learning Automatonbased solution to the dynamic single source shortest path problem. It involves finding the shortest path in a singlesource stochastic graph topology where there are continuous probabilistic updates in the edgeweights. The algorithm is significantly more efficient than the existing solutions, and can be used to find the “statistical” shortest path tree in the “average ” graph topology. It converges to this solution irrespective of whether there are new changes in edgeweights taking place or not. In such random settings, the proposed learning automata solution converges to the set of shortest paths. On the other hand, the existing algorithms will fail to exhibit such a behavior, and would recalculate the affected shortest paths after each weightchange. The important contribution of the proposed algorithm is that all the edges in a stochastic graph are not probed, and even if they are, they are not all probed equally often. Indeed, the algorithm attempts to almost always probe only those edges that will be included in the shortest path graph, while probing the other edges minimally. This increases the performance of the proposed algorithm. All the algorithms were tested in environments where edgeweights change stochastically, and where the graph topologies undergo multiple simultaneous edgeweight updates. Its superiority in terms of the average number of processed nodes, scanned edges and the time per update operation, when compared with the existing algorithms, was experimentally established. The algorithm can be applicable in domains ranging from ground transportation to aerospace, from civilian applications to military, from spatial database applications to telecommunications networking. Index Terms—Algorithm, dynamic, routing, shortest path. I.