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Anticipation in High-Level Cognition

This review meeting explores the role of anticipation in high-level cognition, including anticipation in decision-making, social interactions, and goal-oriented actions. It focuses on the scenarios of guards and thieves and discusses tools and techniques for studying anticipation.

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Anticipation in High-Level Cognition

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  1. Anticipation in High-Level Cognition ISTC-CNR Rino Falcone, Cristiano Castelfranchi, Giovanni Pezzulo, Luca Tummolini, Michele Piunti MindRACESFirst Review MeetingLund, 11/01/2006

  2. Anticipation in High-level Cognition: Outline • Issues and Methodology • Anticipation and Deliberation • Social Anticipation • A Sample Scenario • Guards and Thieves • Tools and techniques MindRACES, First Review Meeting, Lund, 11/01/2006

  3. Anticipation in Deliberation • Anticipation in high-level cognition: • Predictions are matched not only with perceptions, but with Goals (that are not current states of affairs and perhaps will never be) • Goals are anticipatory representations that drive and organize actions • (Strategical) Planning: considering the possible consequences of one’s own actions MindRACES, First Review Meeting, Lund, 11/01/2006

  4. Social Anticipation • Anticipating intentional actions • To cope with the uncertainty of the social environment (Dennett 1991) • Coordination is only possible if agents anticipate each other • For acting on other agents (e.g. blocking, helping, relying) • New Basis for predicting: • Theory of mind (I know he wants to go to…) • Norms, roles, conventions (It is forbidden to go to…) • Categories (Thieves normally do…) • Recognition (of other agent’s Actions-Plans-Goals) • Reputation (People say that…) MindRACES, First Review Meeting, Lund, 11/01/2006

  5. Scenario: Guards and Thieves • In this scenario, robots or agents can have two different roles (guards or thieves). Some objects areconsidered to be valuable and the thief’s aim is to find and pick them all. The thief has a storewhere it places all the valuables it succeeds to take away. The goal of the guard is to protect thevaluables. V G T MindRACES, First Review Meeting, Lund, 11/01/2006

  6. Issues in WP4 TASKS QUESTIONS • How are high-level “decisions” realized by low-level “behaviors”? • How can the control come back from low to high level in case of necessity, e.g. errors? • Skill learning: how are sequences/patterns of actions “compiled” into behaviors? How are concepts “abstracted”? • Integrating different levels of action control (e.g. routinary, reasoning), based on different kinds of expectations (e.g. implicit, explicit) and arbitrating them by shifting level of control or by mediating. • Being able to transform the representations used for the different levels of control; e.g. learning as routinization of behaviours that are first adopted in a deliberative way; or abstracting concepts that are first learned in a trial-and-error way. Interaction between deliberative processes and action control MindRACES, First Review Meeting, Lund, 11/01/2006

  7. An Example TASK: Having two or more conflicting goals (e.g. protect two places), possibly conflicting, and arbitrating between them. The GUARD is in the living room and has two goals: control the bathroom and control the bedroom. CASE 1: replanning CASE 2: intention reconsideration CASE 3: exploiting opportunities G G G G T G MindRACES, First Review Meeting, Lund, 11/01/2006

  8. Prediction of other agents’ behavior What’s special about anticipating intentional agents? Which social skills can be realized only by the means of anticipatory capabilities? Identify single actions and infer the associated plans Anticipation of the adversary behaviour (avoiding-intercepting) by using “social” cues Agents behave in terms of Competition and Cooperation: positive and negative social interference enables agents in reading the world in terms of opportunity/chances or obstacles. Modeling reliance, help, overhelp, delegation, trust Social Issues in WP4 QUESTIONS TASKS MindRACES, First Review Meeting, Lund, 11/01/2006

  9. G1 G2 Some examples: • Reliance, Help:guard#1 patrols Bedroom and Living, guard#2 patrols Living and Bathroom. Guard#1 is moving towards Living. Guard#2 can: • stay in Bathroom relying guard#1 for patrolling Living • Help the other agents (by anticipating their needs): for example, a thief can help another one by distracting the guards G1 MindRACES, First Review Meeting, Lund, 11/01/2006

  10. Issues in WP5 TASKS QUESTIONS • Effects of Surprise: • Cognitive, Behavioural, and Affective Reactions to Expectations (Mis)match with the World • Short Term Effects and Long Term Effects (caution, Accuracy) • Goals to achieve selected because of balance between foreseen Risks, expectations about results, Accuracy about self contained actions [..] • Expected Risks facing with expected environment’s safety (adaption and anticipation about enviroment’s events) • Different threshold of accepted Risk: “cautious” agents accept a less amount of anticipated risks and behave to avoid them. • What does it mean to have an affective reaction? E.g. why/when emotional Agents Behave better than unemotional agents? • Modeling different “Personalities” MindRACES, First Review Meeting, Lund, 11/01/2006

  11. T1 An Example of Perceived Surprise: An unexpected Fire begin to burn close to Thief_1: MindRACES, First Review Meeting, Lund, 11/01/2006

  12. Two Instruments • Jadex: The behavioral and learning function of prediction • Practical Reasoning • Beliefs, Desires, Intentions • AKIRA: The control function of prediction • Hierarchical Architecture • Distributed Control (with Schemas) • Developed in collaboration with NOZE MindRACES, First Review Meeting, Lund, 11/01/2006

  13. Jadex: BDI • Beliefs:declarative knowledge • Desires: world-states that the agent is trying to reach (goals) • Intentions: are the chosen means to achieve the agent’s desires, i.e. sequences of actions (plans) • Key mechanisms: Representation, Processing, Deliberation MindRACES, First Review Meeting, Lund, 11/01/2006

  14. Enhanching BDI • Model of Expectations: explicit expectations are associated to Goals and Actions • Generated on the basis of different sources: • statistical, norms, roles, etc. • Updated on the basis of new knowledge • Actively searching new knowledge • Epistemic Actions: check whether, check what • Used for Goal/Action selection MindRACES, First Review Meeting, Lund, 11/01/2006

  15. INTENTION MANAGER GOALS PLANS SCHEMAS STIMULI AKIRA: hierarchical architecture • Three Layers: • Intention Management • Planning • Actuation and Adaptation • Features: • Conflicting goals, plans and behaviors • Control shift • Different kinds of expectations MindRACES, First Review Meeting, Lund, 11/01/2006

  16. Different levels of expectations • Intentional level: • Expectations are used for goal selection • Planning: • Mismatch is used for replanning or intention reconsideration • Actuation/Adaptation: Schema Mechanism • Many competing Schemas generating predictions • Schemas predicting better are selected for action control • Expectations are used for monitoring, too MindRACES, First Review Meeting, Lund, 11/01/2006

  17. Integration • Attention Mechanisms (WP3): • Some attention mechanisms (focus_on obj_x) will be initially implemented as scripts. We can integrate e.g. attentional shift (a moving object resembling a thief attracts attention) (LUND, IDSIA) • Bases for Predicting: • We assume many bases such as roles, norms, etc. Better statistical and analogical predictive capabilities can be added (IDSIA, NBU) • Learning the Map of the Environment: • Initially, the map is pre-designed; it can be learned (IDSIA, UW-COGSCI, OFAI) • Emotions (WP5): • Integrate our model of surprise with models of other emotions (IST) • Sensorimotor Coupling (WP4): • Some behaviors (reach obj_x) will be initially implemented as scripts MindRACES, First Review Meeting, Lund, 11/01/2006

  18. A bio-inspired approach to study goal-based behaviour ISTC-CNR Gianluca Baldassarre, Dimitri Ognibene MindRACESFirst Review MeetingLund, 11/01/2006

  19. A bio-inspired approach to study goal-based behaviour Castelfranchi (2005):“goals…say us not how the world is …but how the world should be…” …in particular we`ll focus on… 1) Organisms`learning of repertoires ofmotor-primitives based on goals 2) Composition of motor-primitives to produce complex behaviours and role ofanticipation MindRACES, First Review Meeting, Lund, 11/01/2006

  20. Layout • Scenarios: tasks • Scenarios: simulated and real robots • Hints to bio-inspired architecture • Integration MindRACES, First Review Meeting, Lund, 11/01/2006

  21. Looking for an object (scenario 1) …from deliverable D2.2 on scenarios… • “Finding a specific object (Game Room)“The purpose of this task is to find and reach a specific object in the environment (e.g. a red cube). The degree of detail of the description must be sufficient to defineunambiguously a single object, not a class of similar ones.” MindRACES, First Review Meeting, Lund, 11/01/2006

  22. Scenario: tasks • Inspired by Cisek & Kalaska (2005)(physiology of monkeys’ premotor cortex) • Anticipation: - motor-primitives triggered by goals - preparatory activation of pre-motor cortex MindRACES, First Review Meeting, Lund, 11/01/2006

  23. Scenario: tasks • Inspired by Cisek & Kalaska (2005)(physiology of monkeys’ premotor cortex) 2-target task errors match to sample Type of cells: (17%) non-dir., “go” motor signal; (20%) dir., build-up only during cc; (15%) dir. when unambig.; (43%) bi-dir. in sc, dir. in cc MindRACES, First Review Meeting, Lund, 11/01/2006

  24. Scenario: simulated robots • 2D simulator,n-sgment arm • 3D simulator3+1+3 d.f. MindRACES, First Review Meeting, Lund, 11/01/2006

  25. Scenario: real robots • Commercial robotic arm(e.g. Pioneer armby MobileRobots) • Link with EU IP RobotCub (“U.E5 Cognition”, Sandini, Univ.Genoa) MindRACES, First Review Meeting, Lund, 11/01/2006

  26. Problem 1: Learning repertoires…premotor cortex • Graziano, Taylor, Moore (2002)Complex Movements Evoked by Microstimulation of Precentral Cortex MindRACES, First Review Meeting, Lund, 11/01/2006

  27. Problem 2: Composition…basal ganglia • Chevalier, Deniau (1990)Hikosaka et al. (1983, 2000)Role of the basal ganglia in the control ofpurposive saccadic eye movements(releasing mechanism based on double inhibition) • Greybiel (1998)The basal ganglia and chunking of action repertoiresBoeker et al. (1998)Role of the human rostral supplementary motor area and the basal ganglia in motor sequence control Sequence offinger tappingwith right hand (fMRI) . Basal Ganglia (Nucleus Caudatus) SupplementaryMotor Area Rostral part MindRACES, First Review Meeting, Lund, 11/01/2006

  28. Towards (bio-inspired) solutions: possible system architectures Transfer function Architecture’s components Math symbols Trained weights Architecture Ideas from literature * * (or ) 1 0 Action StateGoal Posture controller Kuperstein (1988) Direct Inverse modelling zk Proprio- ception  Decision maker (accumulators) Usher & McClelland (2001) Competitive races,e.g. in premotor cortex 1 0 1 bj - aj al Surprise:St+1=Rt+1-Vt Critic (left) anticipatory go! activation V yj 1 0 Sutton & Barto (1998) Actor-critic models 2 -2 1 0 Actor (right) Perceptual memory Schmidhuber et al. (1997-2002) Long-short term memories (…e.g. as “liquid state machine”) xci rci Retina MindRACES, First Review Meeting, Lund, 11/01/2006

  29. Integration • Memory:long-short term memory(IDSIA) • Vision and attention:Endowing the system with a camera(IDSIA-LUCS) …thanks… MindRACES, First Review Meeting, Lund, 11/01/2006

  30. MindRACES, First Review Meeting, Lund, 11/01/2006

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