Solving non-stationary bandit problems by random sampling from sibling Kalman filters

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6098 LNAI, Issue PART 3, 2010, Pages 199-208

  Granmo, Ole Christoffer ^a   Berg, Stian ^a  

^a UNIVERSITY OF AGDER   (Norway)

Author keywords

Bandit Problems; Bayesian Learning; Kalman Filter

Indexed keywords

BANDIT PROBLEMS; BAYESIAN; BAYESIAN LEARNING; BAYESIAN METHODS; CLASSICAL OPTIMIZATION; FIXED STRATEGY; HYPER-PARAMETER; MULTI-ARMED BANDIT PROBLEM; MULTIPLE ARMS; NON-STATIONARY ENVIRONMENT; NONSTATIONARY; NOVEL SOLUTIONS; OPTIMAL DECISION MAKING; PARAMETER SETTING; RANDOM REWARD; RANDOM SAMPLING; STATIONARY ENVIRONMENTS;

DECISION MAKING; INDUSTRIAL ENGINEERING; INTELLIGENT SYSTEMS; KALMAN FILTERS; MULTI AGENT SYSTEMS; NORMAL DISTRIBUTION; OPTIMIZATION;

BAYESIAN NETWORKS;

EID: 79551524402     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13033-5_21     Document Type: Conference Paper

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