Self-Motivated Behaviour of Autonomous Agents Michal Petrus, Pavel Nahodil, David Kadleček Dept. of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague Technická 2, 166 27 Prague, Czech Republic petrus@lab.felk.cvut.cz, nahodil@ lab.felk.cvut.cz, kadlecd@rtime.felk.cvut.cz INTRODUCTION MOTIVATIONAL SYSTEM We briefly introduce basic concept of our behavior control mechanism based on motivations and describe elementary phases in process of sensor-action assessment. This conception is analogical to behavioural aspects in the decision of natural beings. Motivational system can be described as including reversible processes that are responsible for changes in behavior. The motivation of an autonomous agent depends not only on its’ states, but also on its assasement of the likely consequences of the future activities. As shown on figure the autonomous agent evaluates consequences of its behavior and based on this evaluation decides what to do next – which activity to apply. This figure shows the relations and mutual relations among elementary behavior controller components. Evaluation Behaviour Memory Values Environment State Motivation Social interactions Robot’s interactions Environment interactions Agent behaviour influences: internal states global goal actual motivation environment influence interactions among robots social interactions Preferred architectures present middle way in the sophistic and immediate criterions. The goal-orientedness is also required. This makes possible more complex behaviour. BEHAVIORAL SYSTEM ARCHITECTURE EXPERIMENTS Our concept of control architecture embodies three layers – reactive, cognitive and social layer [3]. Each layer exploits information in the decision-making process on a level corresponding to the character of information emerging from agent interactions with environment. Additional each elementary layer also represents the source of motivational behavior for inferior layer. Thus the all agent’s control architecture include hierarchical motivational system. Described architecture has been utilized in experiments in single agent task like spatial navigation, transport tasks or behavior learning and in multi-robot cooperative tasks that, where is advantageous to use set of robots like distributed map building, distributed space covering, cooperative space exploration,collective learning or robotic soccer. Behavioral agent resposibilities: 1. Reactive features immediate response situattedness 2. Behavior features autonomy adaptivity 3. Deliberative features deliberateness planning Motivation system Internal states Behavior selection module Motivations States values Alerts, priorities Environment perception actions Executive system Sensory system External stimuli Behavior Behaviour consequences REFERENCES [1] Arkin, R. C. – Bekey, G., A..: Robot Colonies. Kluwer Academic Publisher Group, AH Dortrech, The Netherlands, 1997, pp. č. 7–č. 27. [2] Arkin, R. C.: Behaviour-Based Robotics. The MIT Press, 1999, pp. č. 123–č. 173. [3] Petrus, M. - Nahodil, P. – Svatoš, V.: Interactions in Community of Behaviour-Based Agents. Conference EUNITE 2001, pp.57-62, Teneriffe, Spain, 2001, pp. č. 57–č. 62. [4] MCFarland, D. – Bosser, T.: Intelligent behaviour in Animals and Robots. A Bradford Book, The MIT Press, 1993, pp. č. 141–č. 172. [5] Maes, P.: A Bottom-Up Mechanism for Action Selection in a Artificial Creatures. From Animals to Animats: Procceding of theAdaptive Behaviour Conference ’91, Edited by S.Wilson and J.Arcady-Mayer, MIT Press, February 1991 Behavior changes are effected by: external stimuli maturation injury motivation learning