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Towards engaging full-body interaction P.Petta1, A.Staller1, R.Trappl1,2, S.Mantler3, T.Psik3, Z.Szalavári3, M.Gervautz3 HCI’99: Beliefs/Emotions in Adaptive Interface Design 1Austrian Research Institute for Artificial Intelligence 2Dept.Med.Cybernetics & Artificial Intelligence, Univ.of Vienna 3Inst.of Computer Graphics, Vienna Univ. of Technology Summary principled background: – appraisal theory: emotions as (grounded) process threefold use of emotions: – for uptake of stimuli – for selection of action – for expressive behaviour implementation – appraisal integrated into the trionic agent control architecture model – “radically simple” scenario Principled background: appraisal theory the Emotional as concern satisfaction process Concerns curiosity, familiarity, well-functioning, exercising, control, attachment, … Appraisals mediate between occurrence of concern-relevant events and changes of internal action tendencies Action tendencies states of readiness to achieve or maintain a given kind of relationship with the environment: stimulus-driven, not goal-oriented stimulus event appraisal: concern relevance, coping potential action tendency change expressive action Threefold use of emotions: stimulus uptake stimulus uptake at different levels: - in the context of the current behaviour - in the context of the current activity (schema) - in broader context (planning,…) Threefold use of emotions: action selection action tendencies approach (“desire”), avoidance (“fear”), being-with (“confidence”), attending (“interest”), rejecting (“disgust”), non-attending (“indifference”), agonistic (“anger”), interrupting (“surprise”), dominating (“arrogance”), submitting (“humility”), deactivation (“sorrow”), bound activation (“effort”), excitement, free activation (“joy”), inactivity (“contentment”), inhibition (“anxiety”) as separate classification system for behavioral repertory of an agent behavior classes can be derived in a principled way from an analysis of a given lifeworld (including artificial ones) 3-fold use of emotions: expressive behaviour emotion categorization by action tendency change vs. emotion categorization by nature of emotional object: - Highly dependent upon which objects are distinguished and considered important - Well-known example: OCC model 3-fold use of emotions: expressive behaviour “...emotions defined primarily by their object …have no characteristic facial expression and ... their presence cannot be recognized by means of expressive behavior alone” (N.Frijda) action expression problem as particular problem in architectures based solely on the second principle (e.g., the OCC Model) Action tendencies are tied to characteristic expressive behavior Behavior classes share specific expressive characteristics “Emotions” as changes in action tendency Implementation: lessons from control architecture design Advances in particular with respect to layered designs Change in appreciation of the role of the “middle” tier of the established trionic model, the scheduler (“middle-out” model of control): scheduler receives important information from lower level behaviors (“cognizant failure”) & spawns long-term planning activities (cf. e.g. E.Gat’s ATLANTIS) scheduler as natural location for implementation of (a substantial part of) expressive action generation support Implementation: trionic agent architecture Plan library (static) Communications: vision data, animation status parameterized animation selection, emotion system state Scheduler: • event appraisal, • plan activation, • “RAP” selection • “RAP” execution monitoring “Reactive action packages”: • start/context/ending conditions • idle/cleanup procedures • “cognizant failures” Emotion subsystem: action tendency dynamics Implementation: scenario (ALIVE, MIT) “magic video mirror” with back-projection video image mixed with computer generated overlay •unencumbered interaction between human visitor and virtual character based on position, postures, and gestures •main focus on “virtual presence” Implementation: constraints interface & scenario issues avoid specialized scenarios, suggest straightforward, simple interaction balance sensory&behavioural capabilities of the synthetic actor and capabilities elicited of human users Implementation: expressive behavior Animation is assembled in real time from basic motion captured sequences that are blended Dynamic surface texture synthesis in real time, based on current action tendency values Implementation: system architecture VISION Identification of user location, posture&gesture appraisal of user location, posture&gesture AGENT CONTROL action tendency update action selection segmentation video image acquisition motion sequencing texture parameters motion blending texture generation ANIMATION real-time rendering and compositing