Reinforcement-Learning Connectionist Systems                (Computer and Information Science)
{'Code': '05020000', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'IIS', 'LongName': 'Div Of Information & Intelligent Systems'}}
{'SignBlockName': 'Joyce'}
Networks of neuron-like processing units, called                        connectionist systems, have interesting computational properties                making them attractive for both psychological modeling and                      potential application in artificial intelligence.  Recent                       research has led to some promising algorithms for learning in                   such networks.                                                                          This project advances such research by developing a                     mathematically well-founded approach to the design of algorithms                for the particular problem of reinforcement learning in                         connectionist networks of stochastic units.  Particular                         algorithms are developed for learning problems having an                        important temporal component, such as control problems with                     feedback delays of unknown duration or problems involving                       recognition or production of time-varying signals.  These                       algorithms are required to admit a suitable on-line                             implementation, in which the learning occurs within the operating               system.                                                                                 In addition to making advances in the theory of such                    algorithms, this project involves implementing the more promising               candidates and evaluating their performance in simulation                       experiments.  Primary criteria for evaluation are learning                      efficiency and suspectibility to convergence to suboptimal                      states.  In addition, this project explores the applicability of                such algorithms to specific problems in artificial intelligence                 and robotics through the study of suitably scaled-down versions                 of these problems.  Such problems include speech recognition and                adaptive sensorimotor control.
{'FirstName': 'Ronald', 'LastName': 'Williams', 'EmailAddress': 'rjw@ccs.neu.edu', 'StartDate': '06/15/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Northeastern University', 'CityName': 'BOSTON', 'ZipCode': '021155005', 'PhoneNumber': '6173733004', 'StreetAddress': '360 HUNTINGTON AVE', 'CountryName': 'United States', 'StateName': 'Massachusetts', 'StateCode': 'MA'}
{'Code': '6840', 'Text': 'ROBOTICS'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8703566.xml'}