{"pk":32822,"title":"Adaptive Action Selection","subtitle":null,"abstract":"In earlier papers we presented a distributed model of action selection in an autonomous intelligent agent (Maes, 1989a, 1989b, 1991a, 1991b). An interesting feature of this algorithm is that it provides a handful of parameters that can be used to tune the action selection behavior of the algorithm. They make it possible, for example, to trade off goal-orientedness for data-orientedness, speed for quality, bias (inertia) for adaptivity, and so on. In this paper we report on an experiment we did in automating the tuning and run-time adaptation of these parameters. The same action selection model is used on a meta-level to select actions that alter the values of the parameters, so as to achieve the action selection behavior that is appropriate for the environment and task at hand.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Paper Presentations -- Planning and Action","is_remote":true,"remote_url":"https://escholarship.org/uc/item/650020zd","frozenauthors":[{"first_name":"Pattie","middle_name":"","last_name":"Maes","name_suffix":"","institution":"Massachusetts Institute of Technology","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"1991-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/32822/galley/23882/download/"}]}