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Basics of Multi-Agent Systems

Basics of Multi-Agent Systems. ‘ École d’été FOR@C ’. Mai 14 th 2004. Jean-Marc Frayret, Ph.D. and Luis Antonio Santa-Eulalia, MSc. Content. Global Objective Introduction / Context General concepts of agents Multi-Agent Systems Some Applications Final Remarks. Global Objective.

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Basics of Multi-Agent Systems

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  1. Basics of Multi-Agent Systems ‘École d’été FOR@C’ Mai 14th 2004 Jean-Marc Frayret, Ph.D. and Luis Antonio Santa-Eulalia, MSc.

  2. Content • Global Objective • Introduction / Context • General concepts of agents • Multi-Agent Systems • Some Applications • Final Remarks

  3. Global Objective To present some basic high-level principles and concepts related to agents and Multi-Agent Systems (MAS), as well as to present some applications.

  4. Content • Global Objective • Introduction / Context • General concepts of agents • Multi-Agent Systems • Some Applications • Final Remarks

  5. Human being always dreamed with devices that are capable to imitate us • Fiction literature and cinematography Introduction / Context • Engineering and computer science

  6. Origin: Artificial Intelligence Field • Great efforts for the future: Semantic Web and Web Services Introduction / Context • Agent: technology to attend, at least, part of this old human desire • Today: they agent already show great value

  7. X Computer as servants Power of decision • Agents = Human agent • Ex: a travel agent or sales agent Introduction / Context • Agents: beyond the automation…

  8. Content • Global Objective • Introduction / Context • General concepts of agents • Multi-Agent Systems • Some Applications • Final Remarks

  9. (Jennings et al., 1998 apud Jarras and Chaib-draa, 2002, p. 1) General concepts of agents • Definitions • There is not a universally accepted and homogeneous • “An agent is a software system, located in an environment, and which acts in an autonomous and flexible way to achieve the goals for which it was conceived”

  10. = + Agents Code State General concepts of agents • Agent State: it can be its execution state and values of attributes • Code: necessary class to execute the agent • Agent System: platform that can create, interpret, execute, transfer and receive agents Execution Unit = Agent Computational Environment = System of Agents

  11. Agent General concepts of agents • Other characteristics • domain oriented reasoning • sensing and acting • goal oriented • possibility to incorporate intelligence • communication ability • negotiating capacity • collaborative • self-starting • temporal continuity • character • adaptive • mobile

  12. Agent Action Output Sensor Input Environment General concepts of agents • Relations with the Environment Wooldridge (1999) • Ongoing and non-terminating action • Repertoiry of actions

  13. Parunak (1998) Environment Process Environment State Agent A Agent B Agent Process Agent Process Sensors Sensors Agent State Agent State Effectors Effectors General concepts of agents • Interaction of an agent with the environment and the interactions among agents

  14. Accessible vs Inaccessible Deterministic vs Non-Deterministic Episodic vs Non-Episodic Static vs Dynamic Discrete vs Continuous General concepts of agents • Classification of environment properties • Partial or total control of the environment

  15. Agent General concepts of agents • Basic Internal Agent’s Organization Transformation of agent’s data structure in agents lifecycle Dissimilar, identical, body-head agent Similarity Mutability Agents may or may not retain a trace of changes in their state based on their experience Allows reuse of parts Modularity Memory

  16. General concepts of agents • Classification of Agents Based on Franklin, S. and Graesser, A. (1996 )

  17. General concepts of agents • Classification of Agents Based on Franklin, S. and Graesser, A. (1996 )

  18. General concepts of agents • Intelligent Agents • Polemic theme • Capacity to react rationally to a stimuli from the environment • In a unpredictable or open environments • Where there is a significant possibility that actions can fail • Flexibility and adaptability • Ability to represent and manipulate knowledge

  19. X Stationary Agent Agent Agent Agent Agent General concepts of agents • Mobile Agents Mobile

  20. Agent Agent Agent Client Client Client Client Server Server Server Server Traditional Based on Mobile Agents General concepts of agents • Client-Server vs. Mobile Agents

  21. Content • Global Objective • Introduction / Context • General concepts of agents • Multi-Agent Systems • Some Applications • Final Remarks

  22. Content • Global Objective • Introduction / Context • General concepts of agents • Multi-Agent Systems • Definitions • Communication • Development • Limitations and challenges • Some Applications • Final Remarks

  23. (Jarras and Chaib-draa, 2002) Multi-Agent Systems • Broad the concept of individual agent • Definition • “a set of agents that work together and interact in order to accomplish some tasks” • “they use their competences and knowledge to strengthen the capacity of solving problems”

  24. Agent Agent Agent Agent Agent Agent MAS Multi-Agent Systems • Characteristics • Each agent has limited capacities and information of problems resolution • Each one has a partial point of view • The MASs have no global control • All data are decentralized • All calculations are asynchronous

  25. Agent Agent Agent Agent Agent Agent MAS Multi-Agent Systems • Advantages Transformation of agent’s data structure in agents lifecycle High speed Agents may or may not retain a trace of changes in their state based on their experience Allows reuse of parts Modularity Reliability

  26. Agent Agent Agent Agent MAS Multi-Agent Systems • Some Important Mechanisms • Interaction • Cooperation • Coordination • Negotiation • Planning • Communication

  27. Content • Global Objective • Introduction / Context • General concepts of agents • Multi-Agent Systems • Definitions • Communication • Development • Limitations and challenges • Some Applications • Final Remarks

  28. Blackboard Message Agent 1 Agent 2 Adapted from Lucena (2003 ) Multi-Agent Systems • Communication Models • Blackboard Schema

  29. White pages Yellow pages B A Agent 1 Agent 2 Facilitator Multi-Agent Systems • Communication Models • Direct Exchange of Messages Adapted from Lucena (2003 )

  30. Agent Communication Languages • Type of messages to support the communication process • Two main ACLs: • KQML (Knowledge Query and Manipulation Language) • ACL (Agent Communication Language) from FIPA.

  31. Syntax Transport mechanism Ontology Knowledge Query and Manipulation Language (KQML) • Main objective: to allow the knowledge sharing among applications • High level • Allows exchange of messages independent of: KQML

  32. KQML Example Agent 1 Agent 2 Agent 1 Agent 2 KQML – Performatives • Based on a set of performatives • represent the intention of the agents when sending some message (ask-if (< (size chip1) (size chip2))) (reply true)

  33. KQML – Basic Example (tell : sender A : receiver B : content ‘price (ISBN1234567890, 24.59$) : language Prolog : ontology ecommerce : in-reply-to message IDxy123)

  34. Agent Communication Language (ACL) from FIPA • FIPA (Foundation of Intelligent Physical Agents) • Benefited from many technological evolution of KQML • Incorporates a lot of instruments to treat the semantic requirements • The syntax is similar to KQML, but the performatives can be different

  35. ACL/FIPA – Basic Example (inform : sender A : receiver B : content ‘price (ISBN1234567890, 24.59$) : language Prolog : ontology ecommerce : in-reply-to xy123A : conversation ID xy123A)

  36. They establish conditions • Creation of knowledge databases • Allow knowledge manipulation by inference machines Knowledge Representation Languages • Inference machines • able to process knowledge stored in the knowledge BD and interpret it

  37. Ontology • It provides a machine-processable semantics of information sources • Easing the communication between agents • Facilitate the construction of a domain model • Proving • A vocabulary of terms • Specification of its meanings • Relations • Usually organized in taxonomies

  38. Ontology Example • 4 levels Classification Hierarchy • Hierarchy: Segment / Family / Class / Commodity • Representation: NN.NN.NN.NN • Example:

  39. Content • Global Objective • Introduction / Context • General concepts of agents • Multi-Agent Systems • Definitions • Communication • Development • Limitations and challenges • Some Applications • Final Remarks

  40. Micro Issues X • Micro Issues • Objective: define agent’s architecture • Design and construction of agents that enjoy its basic properties MAS Development • MAS are considered complex systems that deserve great effort to develop it Macro Issues

  41. Data input from sensors WORLD AGENT BELIEFS PLANS Interpreter DESIRES INTENTIONS Effector commands Micro Issues • Best-known agent architecture is the Procedural Reasoning System (Woodridge, 1998).

  42. Agent Agent Agent Agent Agent Agent MAS Macro Issues • Objective: How one designs an agent society that can (co)operate effectively • Societies, not individuals • Contract Net • The best-known framework for DPS

  43. A1 A2 A1 A2 A3 A3 A4 A4 a) Recognizing the problem b) Task announcement c) Bidding d) Awarding the contract A1 A2 A1 A2 A3 A3 A4 A4 Contract Net (Woodridge, 1998)

  44. Content • Global Objective • Introduction / Context • General concepts of agents • Multi-Agent Systems • Definitions • Communication • Development • Limitations and challenges • Some Applications • Final Remarks

  45. Some Limitations • A lot of applications that use agents can be developed using other techniques

  46. Some Limitations • More?... • Agents do not have complete global Knowledge about its environment • Globally sub-optimal decisions are common • It can take time until users gain confidence in the agents.

  47. Identify and bring together the disaccording points and conflicting intentions Allow the agents to properly communicate and interact Control or reduce some agents’ behavior that leads to chaotic and oscillatory performance Design technological platforms and development methodologies for the MAS Main Challenges

  48. Content • Global Objective • Introduction / Context • General concepts of agents • Multi-Agent Systems • Some Applications • Final Remarks

  49. Manufacturing systems Telecommunications Financial systems Control systems Entertainment Some Applications • MAS inspires studies related to diverse disciplines, in particular: • sociology, social psychology, cognitive sciences and others

  50. Spell checker in some “Text Editor” Thermostat Electronic mail software Agents in Our Daily Life

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