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- Cao, YJ

Learner classifier systems: Theory and applications - A state-of-the-art review*ENGINEERING INTELLIGENT SYSTEMS FOR ELECTRICAL ENGINEERING AND COMMUNICATIONS*

- Gelenbe, E; Lent, R; Xu, ZG

Measurement and performance of a cognitive packet network*COMPUTER NETWORKS-THE INTERNATIONAL JOURNAL OF COMPUTER AND TELECOMMUNICATIONS NETWORKING*

- Nicolaisen, J; Petrov, V; Tesfatsion, L

Market power and efficiency in a computational electricity market with discriminatory double-auction pricing*IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION*

- Bonarini, A; Bonacina, C; Matteucci, M

An approach to the design of reinforcement functions in real world, agent-based applications*IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS*

- Crabbe, FL; Dyer, MG

Goal directed adaptive behavior in second-order neural networks: The MAXSON family of architectures*ADAPTIVE BEHAVIOR*

- Pipe, AG

An architecture for learning "potential field" cognitive maps with an application to mobile robotics*ADAPTIVE BEHAVIOR*

- Dempster, MAH; Payne, TW; Romahi, Y; Thompson, GWP

Computational learning techniques for intraday FX trading using popular technical indicators*IEEE TRANSACTIONS ON NEURAL NETWORKS*

- Moody, J; Saffell, M

Learning to trade via direct reinforcement*IEEE TRANSACTIONS ON NEURAL NETWORKS*

- Si, J; Wang, YT

On-line learning control by association and reinforcement*IEEE TRANSACTIONS ON NEURAL NETWORKS*

- Anastasio, RJ

Input minimization: a model of cerebellar learning without climbing fiber error signals*NEUROREPORT*

- Svinin, MM; Yamada, K; Ueda, K

Emergent synthesis of motion patterns for locomotion robots*ARTIFICIAL INTELLIGENCE IN ENGINEERING*

- Althoefer, K; Krekelberg, B; Husmeier, D; Seneviratne, L

Reinforcement learning in a rule-based navigator for robotic manipulators*NEUROCOMPUTING*

- Morimoto, J; Doya, K

Acquisition of stand-up behavior by a real robot using hierarchical reinforcement learning*ROBOTICS AND AUTONOMOUS SYSTEMS*

- Park, KH; Kim, YJ; Kim, JH

Modular Q-learning based multi-agent cooperation for robot soccer*ROBOTICS AND AUTONOMOUS SYSTEMS*

- Lee, MR; Rhee, H

The effect of evolution in artificial life learning behavior*JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS*

- Brodie, M; DeJong, G

Iterated phantom induction: A knowledge-based approach to learning control*MACHINE LEARNING*

- Dzeroski, S; De Raedt, L; Driessens, K

Relational reinforcement learning*MACHINE LEARNING*

- Tadic, V

On the convergence of temporal-difference learning with linear function approximation*MACHINE LEARNING*

- Randlov, J; Alstrom, P

Perception control*PHYSICA A*

- Suri, RE; Bargas, J; Arbib, MA

Modeling functions of striatal dopamine modulation in learning and planning*NEUROSCIENCE*

- Ueno, A; Takeda, H

Cooperation of categorical and behavioral learning in a practical solutionto the abstraction problem*NEW GENERATION COMPUTING*

- Asada, M; Uchibe, E

Multiagent learning towards RoboCup*NEW GENERATION COMPUTING*

- Miyazaki, K; Kobayashi, S

Rationality of reward sharing in multi-agent reinforcement learning*NEW GENERATION COMPUTING*

- Su, SF; Hsieh, SH; Chuang, CC

On the study of embedding fuzzy concept into reinforcement learning schemes*JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS*

- Borkar, VS

A sensitivity formula for risk-sensitive cost and the actor-critic algorithm*SYSTEMS & CONTROL LETTERS*

- Seale, DA; Daniel, TE; Rapoport, A

The information advantage in two-person bargaining with incomplete information*JOURNAL OF ECONOMIC BEHAVIOR & ORGANIZATION*

- Bonarini, A; Trianni, V

Learning fuzzy classifier systems for multi-agent coordination*INFORMATION SCIENCES*

- Gelenbe, E; Seref, E; Xu, ZG

Simulation with learning agents*PROCEEDINGS OF THE IEEE*

- Salmon, TC

An evaluation of econometric models of adaptive learning*ECONOMETRICA*

- Atlasis, AF; Vasilakos, AV

Applicability of reinforcement learning algorithms to Usage Parameter Control*COMPUTATIONAL INTELLIGENCE IN TELECOMMUNICATIONS NETWORKS*

- Saltouros, MP; Atlasis, AF; Vasilakos, AV; Pedrycz, W

QoS-based hierarchical routing in ATM networks using reinforcement learning algorithms: A methodology*COMPUTATIONAL INTELLIGENCE IN TELECOMMUNICATIONS NETWORKS*

- Abul, O; Polat, F; Alhajj, R

Multiagent reinforcement learning using function approximation*IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS*

- Bertsekas, DP; Homer, ML; Logan, DA; Patek, SD; Sandell, NR

Missile defense and interceptor allocation by neuro-dynamic programming*IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS*

- Wei, JD; Sun, CT

Constructing hysteretic memory in neural networks*IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS*

- Sun, R; Sessions, C

Self-segmentation of sequences: Automatic formation of hierarchies of sequential behaviors*IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS*

- Muller, JP; Faihe, Y

Toward learning to control robot behaviors*INTELLIGENT AUTOMATION AND SOFT COMPUTING*

- Levin, E; Pieraccini, R; Eckert, W

A stochastic model of human-machine interaction for learning dialog strategies*IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING*

- Clausen, C; Wechsler, H

Quad-Q-learning*IEEE TRANSACTIONS ON NEURAL NETWORKS*

- Yuan, ST; Liu, A

Next-generation agent-enabled comparison shopping*EXPERT SYSTEMS WITH APPLICATIONS*

- Bhanu, B; Lin, YQ; Jones, G; Peng, J

Adaptive target recognition*MACHINE VISION AND APPLICATIONS*

- Distante, C; Anglani, A; Taurisano, F

Target reaching by using visual information and Q-learning controllers*AUTONOMOUS ROBOTS*

- Touzet, CF

Robot awareness in cooperative mobile robot learning*AUTONOMOUS ROBOTS*

- Aydin, ME; Oztemel, E

Dynamic job-shop scheduling using reinforcement learning agents*ROBOTICS AND AUTONOMOUS SYSTEMS*

- Martin, P; Millan, JD

Robot arm reaching through neural inversions and reinforcement learning*ROBOTICS AND AUTONOMOUS SYSTEMS*

- Paletta, L; Pinz, A

Active object recognition by view integration and reinforcement learning*ROBOTICS AND AUTONOMOUS SYSTEMS*

- Garcia-Martinez, R; Borrajo, D

An integrated approach of learning, planning, and execution*JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS*

- Yan, JJ; Tokuda, N; Miyamichi, J

A constructive compound neural networks. II Application to artificial lifein a competitive environment*IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS*

- Arai, S; Miyazaki, K; Kobayashi, S

Controlling multiple cranes using multi-agent reinforcement learning: Emerging coordination among competitive agents*IEICE TRANSACTIONS ON COMMUNICATIONS*

- Iizuka, H; Suzuki, K; Yamamoto, M; Ohuchi, A

Learning of virtual words utilized in negotiation process between agents*IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES*

- Munos, R

A study of reinforcement learning in the continuous case by the means of viscosity solutions*MACHINE LEARNING*

- Menczer, F; Belew, RK

Adaptive retrieval agents: Internalizing local context and scaling up to the Web*MACHINE LEARNING*

- Singh, S; Jaakkola, T; Littman, ML; Szepesvari, C

Convergence results for single-step on-policy reinforcement-learning algorithms*MACHINE LEARNING*

- Gillies, A; Arbuthnott, G

Computational models of the basal ganglia*MOVEMENT DISORDERS*

- Tong, H; Brown, TX

Adaptive call admission control under quality of service constraints: A reinforcement learning solution*IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS*

- Vogiatzis, D; Stafylopatis, A

Reinforcement learning for symbolic expression induction*MATHEMATICS AND COMPUTERS IN SIMULATION*

- Potocnik, P; Grabec, I

Adaptive self-tuning neurocontrol*MATHEMATICS AND COMPUTERS IN SIMULATION*

- Borkar, VS; Meyn, SP

The ODE method for convergence of stochastic approximation and reinforcement learning*SIAM JOURNAL ON CONTROL AND OPTIMIZATION*

- Michalski, A

An application of decision rules in reinforcement learning*CONTROL AND CYBERNETICS*

- Hwang, KS; Chao, HJ

Adaptive reinforcement learning system for linearization control*IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS*

- Zhong, XM; Santos, E

Directing genetic algorithms for probabilistic reasoning through reinforcement learning*INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS*

- Takeda, M; Nakamura, T; Imai, M; Ogasawara, T; Asada, M

Enhanced continuous valued Q-learning for real autonomous robots*ADVANCED ROBOTICS*

- Potapov, AB; Ali, MK

Learning, exploration and chaotic policies*INTERNATIONAL JOURNAL OF MODERN PHYSICS C*

- De Farias, DP; Van Roy, B

On the existence of fixed points for approximate value iteration and temporal-difference learning*JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS*

- Oh, SY; Lee, JH; Choi, DH

A new reinforcement learning vehicle control architecture for vision-basedroad following*IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY*

- Unsal, C; Kachroo, P; Bay, JS

Multiple stochastic learning automata for vehicle path control in an automated highway system*IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS*

- Lin, CT; Chung, IF

A reinforcement neuro-fuzzy combiner for multiobjective control*IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS*

- Yung, NHC; Ye, C

An intelligent mobile vehicle navigator based on fuzzy logic and reinforcement learning*IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS*

- Lin, CT; Jou, CP

Controlling chaos by GA-based reinforcement learning neural network*IEEE TRANSACTIONS ON NEURAL NETWORKS*

- Santos, JM; Touzet, C

Dynamic update of the reinforcement function during learning*CONNECTION SCIENCE*

- Riedmiller, M

Concepts and facilities of a neural reinforcement learning control architecture for technical process control*NEURAL COMPUTING & APPLICATIONS*

- Balkenius, C; Moren, J

Dynamics of a classical conditioning model*AUTONOMOUS ROBOTS*

- Martin, P; Millan, JDR

Learning of sensor-based arm motions while executing high-level descriptions of tasks*AUTONOMOUS ROBOTS*

- Wiering, M; Salustowicz, R; Schmidhuber, J

Reinforcement learning soccer teams with incomplete world models*AUTONOMOUS ROBOTS*

- Hougen, DF; Rybski, PE; Gini, M

Repeatability of real world training experiments: A case study*AUTONOMOUS ROBOTS*

- Peng, J; Bhanu, B

Learning to perceive objects for autonomous navigation*AUTONOMOUS ROBOTS*

- Contreras-Vidal, JL; Schultz, W

A predictive reinforcement model of dopamine neurons for learning approachbehavior*JOURNAL OF COMPUTATIONAL NEUROSCIENCE*

- Santos, JM; Touzet, C

Exploration tuned reinforcement function*NEUROCOMPUTING*

- Johannet, A; Sarda, I

Goal-directed behaviours by reinforcement learning*NEUROCOMPUTING*

- Salum, C; da Silva, AR; Pickering, A

Striatal dopamine in attentional learning: A computational model*NEUROCOMPUTING*

- Cauwenberghs, G

A nonlinear noise-shaping delta-sigma modulator with on-chip reinforcementlearning*ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING*

- Wilkinson, J; Levinson, R

Research environment for data analysis tool allocators*APPLIED INTELLIGENCE*

- Sun, R; Peterson, T; Merrill, E

A hybrid architecture for situated learning of reactive sequential decision making*APPLIED INTELLIGENCE*

- Arai, Y; Fujii, T; Asama, H; Kaetsu, H; Endo, I

Collision avoidance in multi-robot systems based on multi-layered reinforcement learning*ROBOTICS AND AUTONOMOUS SYSTEMS*

- Yoshioka, T; Ishii, S; Ito, M

Strategy acquisition for the game "Othello" based on reinforcement learning*IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS*

- Yoshida, T; Hori, K; Nakasuka, S

Learning the balance between exploration and exploitation via reward*IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES*

- Zhao, G; Tatsumi, S; Sun, R

RTP-Q: A reinforcement learning system with time constraints exploration planning for accelerating the learning rate*IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES*

- Hopkins, E

Learning, matching, and aggregation*GAMES AND ECONOMIC BEHAVIOR*

- Doya, K

What are the computations of the cerebellum, the basal ganglia and the cerebral cortex?*NEURAL NETWORKS*

- Samejima, K; Omori, T

Adaptive internal state space construction method for Reinforcement learning of a real-world agent*NEURAL NETWORKS*

- Sun, R; Peterson, T

Multi-agent reinforcement learning: weighting and partitioning*NEURAL NETWORKS*

- Meuleau, N; Bourgine, P

Exploration of multi-state environments: Local measures and back-propagation of uncertainty*MACHINE LEARNING*

- Baum, EB

Toward a model of intelligence as an economy of agents*MACHINE LEARNING*

- Konda, VR; Borkar, VS

Actor-critic-type learning algorithms for Markov decision processes*SIAM JOURNAL ON CONTROL AND OPTIMIZATION*

- Dreyfus-Leon, MJ

Individual-based modelling of fishermen search behaviour with neural networks and reinforcement learning*ECOLOGICAL MODELLING*

- Hasegawa, Y; Fukuda, T; Shimojima, K

Self-scaling reinforcement learning for fuzzy logic controller - Applications to motion control of two-link brachiation robot*IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS*

- Morimoto, J; Doya, K

Hierarchical reinforcement learning for motion learning: learning 'stand-up' trajectories*ADVANCED ROBOTICS*

- Iida, F; Hara, F

Behavior learning of a face robot based on the characteristics of human instruction*ADVANCED ROBOTICS*

- Micera, S; Sabatini, AM; Dario, P

Adaptive fuzzy control of electrically stimulated muscles for arm movements*MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING*

- Das, TK; Gosavi, A; Mahadevan, S; Marchalleck, N

Solving semi-Markov decision problems using average reward reinforcement learning*MANAGEMENT SCIENCE*

- Camerer, C; Ho, TH

Experience-weighted attraction learning in normal form games*ECONOMETRICA*

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Documento generato il 13/08/20 alle ore 05:20:22