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Intelligent agents ■ Area on the border(s) of computer science Intelligent Agents (distributed systems), SE, AI, economics, ethology, cognitive science... ■ Beware: agents do not always mean the same across the literature! ■ Metaphoric (?!) use of cognitive notions for describing intelligent systems Prof. Dr. J.-J. Ch. Meyer Utrecht University 2 1 Intelligent agents: our definition Properties of agents ■ Software / hardware entities that display ■ Situated: acting in an environment ■ Reactive: able to react adequately to a certain degree of autonomy / initiative, are proactive/goal-directed ■ Mostly described in terms of having ‘mental states’ (‘strong’ notion of agency) ■ Show informational and motivational attitudes (unexpected) situations arising in the environment ■ Pro-active: setting/pursuing its own goals ■ Social: able to co-operate with other agents 3 4 Agents versus objects Multi-Agent Systems (MASs) ■ objects: some autonomy (e.g. own ■ A MAS is a group of agents, together methods) ■ agents more ‘subjects’ than ‘objects’ ■ “objects do it for free, agents do it for money (or because they want to)” ■ “agents are objects with an attitude” with their common, shared environment ■ Primary aim: construction of flexible, robust systems, i.e. able to cope with unexpected events in the environment (without crashing or displaying other undesired behavior) – ‘graceful degradation’ 5 6 1 (Why) MASs ? Why MASs ? (ctd) ■ Flexibility & robustness should be ■ Besides, from an engineering obtained via the properties of individual agents: perspective, the agent’s metaphor (i.e. using agent concepts metaphorically) helps to design and construct complicated (distributed) systems!! – Reactivity – Pro-activeness – Social ability 7 8 Applications of (multi-)agent systems (Multi) Robot Systems ■ Autonomous Robots ■ Traffic & transport ■ Softbots (Software agents) – Industrial applications – Commercial applications – Medical applications – Entertainment ■ Space robots ■ Rescue robots ■ Robot soccer ■ Robot companions ■ … 9 Robotic System ROBOT Cognitive robotics ■ Cognitive Robotics (CR) concerns the CONTROLLER percepts theory and implementation of robots that reason, act and perceive in changing, incompletely known, unpredictable environments commands BODY stimuli 10 actions ENVIRONMENT 11 12 2 Cognitive robotics Cognitive robotics “Cognitive robotics aims at creating robots - or, more generally, any kind of largely autonomous agents - which are endowed with high-level cognitive functions.” ■ Cognitive robots must have higher-level Hölldobler & Tielscher cognitive functions that involve reasoning about: – Goals, actions, time – When to perceive & what to look for – Cognitive states of other agents – Collaborative task execution 13 14 High-level cognitive functions What is cognitive robotics? ■ reasoning about actions and effects ■ CR is concerned with integrating – Reasoning – Perception – Action ■ perceptions ■ goals and desires ■ ‘mental state’ of other agents within a uniform theoretical and implementation framework Cognitive robotics : viewing robots as intelligent agents 15 What is cognitive robotics? 16 Cognitive Robotic System ■ In other words: ROBOT Cognitive Robotics takes “The robot as intelligent agent” as a perspective: “Robots are autonomous” AGENT percepts commands BODY stimuli actions ENVIRONMENT 17 18 3 NASA explorer robots (Multi) Robot Systems 19 (Multi) Robot Systems 20 Autonomous vehicles Autonomous Unmanned Aerial Vehicle - Linköping 21 Linköping’s UAV project 22 Robot soccer 23 24 4 25 26 Intelligent Robot Companions RoboCup 2014 simulation league 2D ■ Companions of human users – Personal assistants RoboCup2014 • PSA’s for ISS (NASA) – Ambient intelligent systems – e-buddies / e-partners • E.g. for elderly people and children 27 Applications of (M)ASystems 28 Applications of (M)AS (2) ■ Industrial Applications – Process control – Manufacturing – Air-traffic control ■ Commercial Applications – Information management – E-commerce – Business process management ■ Medical Applications – Patient monitoring – Health care ■ Entertainment – Games: virtual characters – Interactive theatre and cinema • Believable agents 31 32 5 Virtual characters in games Contents lectures (GATE subproject with TNO and UT) ■ Aim: to make more interesting games ■ Model virtual character as a ‘BDI+’ agent ■ Sub-subprojects: – Mental state abduction ■ Introduction ■ Philosophical foundations ■ Logical foundations of actions and agents • Application of a ‘theory of mind’ for agents • Michal Sindlar – Single-Agent Logics – Explainable AI ■ Reasoning about action & change • Use BDI reasoning for letting agents in a training environment (serious gaming) explain their actions • Maaike Harbers ■ Multi-Agent Logics 33 34 Literature on philosophical foundations Philosophical Foundations – P.R. Cohen & H.J. Levesque, Intention is Choice with Commitment, Artificial Intelligence 42, 1990, pp. 213-261 leesmap – A.S. Rao & M.P. Georgeff, Modelling rational agents within a BDI-architecture, in: Proc. KR’91, Morgan Kaufmann, 1991website ■ Dennett’s intentional stance ■ Bratman’s theory of intentions ■ Cohen & Levesque’s approach ■ Rao & Georgeff’s BDI logic – M.E. Bratman, Intention, Plans, and Practical Reason, Harvard U.P., 1987* 35 36 Logical foundations of actions and agency Reasoning about action & change ■ Intelligent agents, agent attitudes ■ Frame problem and related problems ■ Temporal logic ■ Situation calculus, event calculus, fluent ■ Dynamic logic calculus ■ Planning ■ Relation with non-monotonic reasoning ■ Single-Agent Logics: BDI, KARO 37 38 6