WP6 - D6.1 Design of integrated models ISTC-CNR September, 26/27, 2005 ISTC-CNR September, 26/27, 2005

2 The ISTC-CNR scenarios (some examples from D2.1) Guards and Thieves Scenario  Task 1: Conflict in accessing the valuables (individual)  Model the shift between deliberative and automatic control of action  Design a cognitive system that is able to exploit representations at different abstractions (different *formats*?)  Task 2: Conflict in accessing the valuables (social)  Identify different basis of prediction (i.e. mind-reading)  Design a cognitive system that is able to help and to do critical help by anticipating other’s needs, actions or capabilities, e.g. by removing obstacles or doing part of other’s work  Design a cognitive system that is able to delegate by trusting Finding and Looking for Scenario  Task 1: Finding a specific object  Recognition of objects from sensory flow on the basis of prediction  Integration of sensory flow in time  Task 2: Finding members of a class of objects by class description  Robustness with respect to contraction or expansions of sensory flow  Abstraction

3 The general mechanisms General MechanismsUse in the ScenarioAvailability StatusPartner BDI based reasoning- Deliberation - Intention reconsideration - Knowledge representation Prototype (JadeX)ISTC-CNR Schema Mechanism based on Fuzzy Logic - Arbitration between conflicting goals - Matching between representations PrototypeISTC-CNR NOZE Belief Networks (bayesian and fuzzy) - Knowledge representation and dynamics PrototypeISTC-CNR

4 The predictive mechanisms Predictive Mechanisms and type Use in the ScenarioAvailability StatusPartner Forward model (based on Fuzzy Cognitive Maps) - Learn to predict - Prediction of the next event at different level of abstraction (sensory input, consequence of action, direct experience, theory- based, indirect experience, simulation) PrototypeISTC-CNR Hebbian time rules- Predictive LearningUnder developmentISTC-CNR Bayesian algorithms Assumption based Truth- Maintenance Systems (ATMS) - Belief revision and update - Prediction based on abduction process - Expected utility In progressISTC-CNR Neural Networks trained with supervised algorithms - Prediction at a single temporal level - Approximation to noise PrototypeISTC-CNR Hierarchical Neural Networks trained with supervised algorithms - Prediction at different temporal and spatial levels PrototypeISTC-CNR

5 The anticipative mechanisms Anticipatory Mechanisms Use in the ScenarioAvailability statusConnection with predictive mechanisms Partner Deliberation Means-end reasoning Planning - Choice of an action or a course of action In progressBayesian algorithms Assumption based Truth- Maintenance Systems (ATMS) ISTC-CNR Schema mechanism- Schema selection - Monitoring and control PrototypeForward models (based on Fuzzy Cognitive Map) ISTC-CNR Qualitative decision making (Logics) - Surprise - Interpretation of the next stimulus via abductive processes In progressBayesian algorithms Assumption based Truth- Maintenance Systems (ATMS) ISTC-CNR Integration of sensory flow via prediction - CategorizationIn progress (missing: robustness with respect to time contraction and expansion) Neural Networks trained with supervised algorithms ISTC-CNR Integration of sensory flow at multiple levels of time abstraction - Hierarchical categorization In progressHierarchical Neural Networks trained with supervised algorithms ISTC-CNR

6 Integration Integrable anticipatory mechanism(s) from other participants Possible integrationEvaluation of the resulting integration Partner Reinforcement learning of epistemic actions From stereotyped to controlled epistemic behaviour Maximising the information value Outperform the stereotyped cognitive systems in recognition tasks IDSIA ?Integration with context information LUCS? NBU? ?Integration with focusing and attentional capabilities (salience maps) LUCS?

7 Three kinds of integration Vertical Combination Horizontal Combination Vertical integration Horizontal integration