MindRACES@Lisbon 1

WP6 - D6.1Design of integrated

models

ISTC-CNRSeptember, 26/27, 2005

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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

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The general mechanisms

General Mechanisms Use in the Scenario Availability Status Partner

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

Prototype ISTC-CNR NOZE

Belief Networks (bayesian and fuzzy)

- Knowledge representation and dynamics

Prototype ISTC-CNR

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The predictive mechanismsPredictive Mechanisms

and typeUse in the Scenario Availability Status Partner

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)

Prototype ISTC-CNR

Hebbian time rules - Predictive Learning Under development ISTC-CNR

Bayesian algorithms Assumption based Truth-Maintenance Systems (ATMS)

- Belief revision and update- Prediction based on abduction process- Expected utility

In progress ISTC-CNR

Neural Networks trained with supervised algorithms

- Prediction at a single temporal level- Approximation to noise

Prototype ISTC-CNR

Hierarchical Neural Networks trained with supervised algorithms

- Prediction at different temporal and spatial levels

Prototype ISTC-CNR

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The anticipative mechanisms

Anticipatory Mechanisms

Use in the Scenario Availability status Connection with predictive

mechanisms

Partner

DeliberationMeans-end reasoningPlanning

- Choice of an action or a course of action

In progress Bayesian algorithms Assumption based Truth-Maintenance Systems (ATMS)

ISTC-CNR

Schema mechanism - Schema selection- Monitoring and control

Prototype Forward models (based on Fuzzy Cognitive Map)

ISTC-CNR

Qualitative decision making (Logics)

- Surprise - Interpretation of the next stimulus via abductive processes

In progress Bayesian algorithms Assumption based Truth-Maintenance Systems (ATMS)

ISTC-CNR

Integration of sensory flow via prediction

- Categorization In 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 progress Hierarchical Neural Networks trained with supervised algorithms

ISTC-CNR

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Integration

Integrable anticipatory

mechanism(s) from other participants

Possible integration Evaluation 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?

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Three kinds of integration

• Vertical Combination• Horizontal Combination• Vertical integration• Horizontal integration