Artificial Intelligence and Multi-Agent Systems Ana García-Serrano PROMAS, AL3 TF2 Ljubljana, 28 Feb. 2005

1. FACULTAD DE INFORMATICA ___________________ UNIVERSIDAD POLITECNICA DE MADRID Artificial Intelligence and Multi-Agent Systems Ana García-Serrano PROMAS, AL3 TF2 Ljubljana, 28 Feb. 2005

2. Software Engineering and Knowledge Engineering (AI) Procedures (systematic operation) Heuristics or declarative procedures • Incomplete specifications • The data, knowledge and solving methods belongs to the experts • Lack of documentation • Complex specifications • Well known Data and Techniques to solve the problem • Documentation - The knowledge and the inference methods are different entities - Explanation capabilities Mixed data and procedure Determinists: Same output for the same input Non-deterministic USE ONLY WHEN NEEDED!

3. Intelligent agent (from AOSE AL3 TF2) As the systems becomes complex it is needed abstractions and metaphors to explain their operations. INTELLIGENT AGENT A cognitive agent that is proactive (through an analytical or reactive operations ie decision) and use a representation of the environment AND - has a representation of N (possible 0) other agents (users or agents) - is endowed with an extensive domain model AND ALSO … Learning (acquire the knowledge it needs to his operation/reasoning) Deep Understanding of emotions… BUT … the designer has to recognise the opportunity for employing an intelligent agent and trust on its competences We don’t want to solve all the problems in AI: BUILD USEFUL AGENTS!

4. Anatomy of a cognitive-intelligent agent COMMUNICATION LAYER KNOWLEDGE METHODS * Reasoning about problems. * Perception of the virtual or physical environment * Interaction Protocols - subtasks assignment - resources competition - sharing of tasks • * Capacity for problem identification • * Internal capacity of problem solving: reactive or analytical • * Knowledge about the • (perceived/ known) structure • of other agents • * Strategic knowledge for rational decisions (agenda, utilities) Individual model Social model AGENDA: prioritized sequence of tasks, Ti, Tj, Tk, Tp, ...

5. Agent-based Engineering • Agent concept infashion during last decade as any software system • rational and autonomous action in a (changing) environment • able to interact into a network (of possible 0 nodes): • Agent based systems (problem centred approach) • A very useful paradigm to cope with dynamic interactions between distributed resources, distributed task execution, legacy systems… • Sets of benevolent agents with shared goals • The modularity allows changes and facilitates the upgrade and recovering from unexpected situations • USE WHEN REALLY NEEDED! (lower cost of centralized solution)

6. Intelligent assistance to e-commerce: The ADVICE project IS THERE A PROBLEM TO SOLVE? The e-commerce solutions has to be improved given that: • Mainly focus on the presentation of goods • The interaction is guided by the user • From the customer point of view: the search and the selection of products is a difficult task due to the lack of assistance IS ADECUATE THE USE OF Intelligent AGENTS? • Anagent-based architecture reflects the conceptual and functional distribution of the decision support installed as a top layer of legacy system: • Intelligent agent to model the sales business • Interaction agent to user/system mixed initiative • Interface agent to multimedia input/output that satisfies the user

7. Knowledge engineering: Prolog ¿enough? 18 com. acts 37 states 90 transitions 34 relaxed patterns 150 tokens 45 templates XML file 1132 lines 4 Product categories 23 products 12 different features Fluid communication with the user Expressive input and output A good quality information to the user Knowledge engineering ATN Decision tree Multimedia planner Ontology KB Rules

8. Working prototype in Ciao Prolog and Java

9. RMI REGISTRY RMI REGISTRY Java Java ciao ciao Register(InA1) Register(int1) Register(InA2) Register(int2) Interface Ag 1 create create DPC 1 New_user USER 1 New_int Interfaces Manager Interaction Manager New_int create New_user create DPC 2 Interface Ag 2 USER 2 Working prototype in Ciao Prolog and Java (multi-user)

10. MAS Engineering • Agent concept infashion during last decade as any software system • rational and autonomous action in a (changing) environment • able to interact into a network (of possible 0 nodes): • Multi-agent systems (interaction centred approach) • A very useful paradigm to the deployment of inherently complex (no-structured) applications in inherently distributed environments • Heterogeneous agents in any kind of organization or society • Harmonizationof the interaction betweenactive agents USE WHEN REALLY NEEDED! (the centralized solution is better

11. MAS: Traffic Control Agents TRYSA2 • TRYSA2: convert the original “benevolent” TRYS agents into “rational” agents • TRYS embedded agents produces • executable local signal plans with • local utility value • Structural co-operation • - Normative layer: permissions and prohibitions • to use control devices • - Social layer: distributed search for the global signal plan that corresponds to the bargain outcome (efficient and fair) • Robust and scalable solution reaching a lower quality solution than the agent based with coordinator a gent a gent a gent agent - 1.000 lines of C++ - 5.000 lines of prolog - 500 lines of Tcl/Tk

12. Traffic Control Agents TRYS Generic Structure of the TRYS decision model Coordination Agent: integrates local control proposals into global consistent signal plans

14. AI the representation and management of the data (reasoning) conditions the way of solving problems MAS MAS + AI the way of interacting conditions the way of solving problems instead of traditional the way of solving problems conditions the way of interacting The reasoning about the way of interacting as a requirement to decide the way of solving problems Comments ... J U S T W H E N N E E D E D ¡