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FACULTAD DE INFORMATICA ___________________ UNIVERSIDAD POLITECNICA DE MADRID Artificial Intelligence and Multi-Agent Systems Ana García-Serrano PROMAS, AL3 TF2 Ljubljana, 28 Feb. 2005 Software Engineering and Knowledge Engineering (AI) Procedures (systematic operation) - Complex specifications - Well known Data and Techniques to solve the problem - Documentation Mixed data and procedure Determinists: Same output for the same input Heuristics or declarative procedures - Incomplete specifications - The data, knowledge and solving methods belongs to the experts - Lack of documentation - The knowledge and the inference methods are different entities - Explanation capabilities Non-deterministic USE ONLY WHEN NEEDED! 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! Anatomy of a cognitive-intelligent agent COMMUNICATION LAYER KNOWLEDGE * Capacity for problem identification Individual model * Internal capacity of problem solving: reactive or analytical Social model * Knowledge about the (perceived/ known) structure of other agents METHODS * Reasoning about problems. * Perception of the virtual or physical environment * Interaction Protocols - subtasks assignment - resources competition - sharing of tasks * Strategic knowledge for rational decisions (agenda, utilities) AGENDA: prioritized sequence of tasks, Ti, Tj, Tk, Tp, ... Agent-based Engineering Agent concept in fashion 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) 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? An agent-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 Knowledge Knowledge engineering engineering: Prolog ¿enough? 34 relaxed patterns 150 tokens 45 templates Expressive input and output 18 com. acts 37 states 90 transitions Fluid communication with the user XML file 1132 lines 4 Product categories 23 products 12 different features A good quality information to the user ATN Multimedia planner Decision tree Ontology KB Rules Working prototype in Ciao Prolog and Java Interface Agent Java layer NL Analyser streams of speech acts regist(new_NLAg) Java Layer streams of speech acts XML Java RMI streams of speech acts NL Generator Dialogue Processing Components Interface Manager Java RMI Interaction Agent problem solving tasks Intelligent Agent offer tree XML Java RMI RMI REGISTRY regist(new_InA) RMI REGISTRY Working prototype in Ciao Prolog and Java (multi-user) RMI REGISTRY RMI REGISTRY Register(InA1) Register(InA2) Register(int1) Register(int2) USER 1 DPC 1 ciao New_user create create Interfaces Manager New_user create New_int Interaction Manager New_int create Java Interface Ag 2 USER 2 Java Interface Ag 1 DPC 2 ciao MAS Engineering Agent concept in fashion 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 • Harmonization of the interaction between active agents USE WHEN REALLY NEEDED! (the centralized solution is better 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 agent agent agent 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 agent - 1.000 lines of C++ - 5.000 lines of prolog - 500 lines of Tcl/Tk Traffic Control Agents TRYS Generic Structure of the TRYS decision model ... Agent 1 Problems Knowledge Problem Scenarios Control Actions Knowledge Behaviour Knowledge Demand Model Traffic Network Management Control Actions Physical Structure Agent N Problems Knowledge Problem Scenarios Co-ordinator Control Actions Knowledge Behaviour Knowledge Demand Model Control Actions Physical Structure Proposals Compatibility Knowledge Agents Priority Knowledge Proposals Completion Knowledge Coordination Agent: integrates local control proposals into global consistent signal plans Object O Artificial Intelligence (inference as computation) * Solve realistic and business problems * Added value (with additional functionalities) yes Programming language and methodology Expert systems Fuzzy reasoning Knowledge manag. Prolog Agent technology Modular Decentralized MAS Dynamic environ. Ill structured Combi. Complexity C++ Java, Corba (Modifiable modules) (Proactivity: comm. remote execution) ??? (Continuous changes) (Under Specification) * Pragm. experience and opportunity to use agents * Supporting tools for non academics yes More than toy applications? No for general use E-commerce … ??? Scheduling … Deployment platforms Comments ... J U S T W H E N AI the representation and management of the data (reasoning) conditions the way of solving problems MAS the way of interacting conditions the way of solving problems instead of traditional the way of solving problems conditions the way of interacting N E E MAS + AI D E The reasoning about the way of interacting as a requirement to decide the way of solving problems D ¡