1. DARE Extended Architecture applied to a Multi-Agent World Márcia Maçãs

2. DARE* Architecture Somatic Marker Theory (António Damásio) –Decision as result of scenario anticipation based on past experience associated with body states that qualify them. * Desenvolvimento de Agentes Robóticos Emocionais. Double Sensorial Processing (Joseph Ledoux) –Fast, simple and reactive processing –Slow, complex and informed processing Goal of DARE Extension: –Apply the architecture to a dynamic multi-agent world; –Obtain adequate individual and social behaviours;

3. New Concepts Sympathy –Human ability to recognise others emotions, acquired by self- awareness of their own emotions [Goleman, 1996]. –Evaluate others and predict their reactions based on a mental model which relates physical expressions with feelings and intentions, acquired by own experience or by observation. Non Verbal Communication –Emotional Expression Implicit Involuntary Informative Verbal Communication

4. Extended DARE Architecture Agent Stimuli World Action Effects Perceptual Analysis Symbolic AnalysisCognitive Analysis Built-in Features and Meanings Memory Body

5. Multi-Agent World - Market Product Examples Agents Expressions Interface

6. Body or Internal State Agents Body or Internal State: Internal State at instant t Ideal Internal State Unbalance at instant t

7. Perceptual Analysis Perceptual Layer Extraction of Relevant Features Ip Perceptual Evaluation DVp Action Selection Action Effects Stimuli World Body Built-in Features and Meanings - Weights relate colours and nutrients - Ip and current Internal State qualify stimuli - DVp Best Dv - Incentive Stimulus Reactive Selection given Ip, DVp e IS

8. Perceptual Layer Action Effects All data is stored in memory, including: The action effects on Internal State are used to adapt (temporarily or not) the meaning functions from which results the DVp.

9. Perceptual Layer - Results 20 Simulations, 500 cycles of 5 seconds Number of Internal State Changes Mean of Maximum Unbalance

10. Conditioned Execution Cognitive Layer Cognitive Analysis Feature Extraction Ic Cognitive Evaluation Actions & Body Action Selection Action Effects Stimuli World BodyMemory DVp intervals that reveal urgency: Stimulus x IS Search in memory for the best action to execute given the current IS and past effects. -Memory structure – Sequences -Selection of action belonging to matched sequence which minimizes the unbalance in the end.

11. Results 20 Simulations, 500 cycles of 5 seconds Number of Internal State Changes Mean of Maximum Unbalance

12. Conditioned Execution Symbolic Layer Symbolic Analysis Feature Extraction Is Symbolic Evaluation Actions & Body Action Selection Action Stimuli World BodyMemory Action Effects

13. Ontology Maps features and symbols: Descriptive symbols (set of features) Identifier symbols (name of a set of descriptive symbols) Syntactic knowledge about messages. Symbolic Layer Expanded Internal State Elements not directly related to survival Internal Representation of the Agent itself Necessary for sympathy and non-verbal communication There is an image of itself on each layer

14. Symbolic Layer Symbolic Evaluation and Action Selection –Specific Actions in this Layer: Communication. –Memory includes Symbolic information Expanded Internal State –Sequences include: Own experience Other agents observed experience –Dialogs that end with expression change –Action is executed if belongs to sequence with an ending expression similar to the ideal one

15. Symbolic Layer - Results

17. Order Long term goals and cooperation Ask for product If supplier has good recall of the product sympathises and starts negotiation. Supplier assumes a fictitious internal state identical to the past and searches the product. Symbolic Layer - Results

18. Conclusion and Future Work The adaptation on the perceptual layer allows a best performance of the overall architecture; The cognitive layer allows flexible behaviour and more accurate learning; The symbolic layer allows communication and the emergence of cooperation/concurrence behaviours. In the application of DARE architecture to a multi-agent world: Future Work Long term anticipation of the internal state. Inference mechanisms triggered by internal stimuli. Learning of Expression-Internal state relation. Improvements on memory managing