Presentation_DAI
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The document provides an overview of distributed artificial intelligence and multi-agent systems. It discusses topics such as the definition of DAI, types of multi-agent systems, interaction among agents, the Agent Communication Language KQML, basic models of communication, and the definition of an agent. It also covers concepts like reactive agents, cognitive agents, classification of agents, and applications of DAI.
Presentation_DAI
- 1. Seminar On Distributed Intelligence By: Bandeep Singh(047/CSE) Department of Computer Science & Engineering Guru Tegh Bahadur Institute of Technology Guru Gobind Singh Indraprastha University Kashmere Gate,New DelhiYear 20010-2011
- 2. Topics • Introduction to DAI • Multi-agent Systems • Types of Multi-agent Systems • Interaction among Agents • KQML • Basic Models of Communication • Meta-Agent
- 3. ARITFICIAL INTELLIGENCE – “the study and design of intelligent agents” Artificial Intelligence (AI) is the area of computer science focusing on creating machines that can engage on behaviors that humans consider intelligent "the science and engineering of making intelligent machines. - John McCarthy
- 4. AI contd. The ability to create intelligent machines has intrigued humans since ancient times, and today with the advent of the computer and 50 years of research into AI programming techniques, the dream of smart machines is becoming a reality. Researchers are creating systems which can mimic human thought, understand speech, beat the best human chessplayer, and countless other feats never before possible. Find out how the military is applying AI logic to its hi-tech systems, and how in the near future Artificial Intelligence may impact our lives.
- 6. DAI Distributed artificial intelligence (DAI) is a subfield of Artificial intelligence research dedicated to the development of distributed solutions for complex problems regarded as requiring intelligence. DAI is closely related to and a predecessor of the field of Multi-Agent Systems
- 7. Torsun: “Distributed Artificial Intelligence is a branch of Artificial Intelligence that is concerned with cooperative problem solving by a decentralized group of collaborating agents. Agents are logically independent and autonomous, they reason, plan, learn and communicate” distributed system of a collective architecture (domain of distributed problem solving), decomposes the problem among collection of autonomous, cooperating, knowledge sharing agents, case specific distributed system of an integration architecture (a multi-agent systems) integrates number of heterogeneous agents of a different nature, complexity reduction
- 8. Agent Jennings: "An agent is a computational system, situated in some environment, that is capable of intelligent, autonomous action in order to meet its design objectives." cooperate learn autonomous interface agent collaborative agent collaborative learning agent intelligent agent
- 9. Agent’s Intelligence Reactivity – Agents ability to provide intelligent responses to percepts and agent senses from the environment (user interface) Proactivity – Agents ability to maintain long term intention, organize its behavior in order to meet targeted goals Social Intelligence –Their ability to perform reasoning about other agents abilities, intentions, current status and possible future course of actions Reactive Agent – agent presenting a reactive intelligence only Cognitive Agent – manipulate symbolic model about their own status, and capabilities (proactivity) and about its collaborative environment
- 10. Agent’s Abstract Architecture • Agent’s Communication Wrapper – translation to and from ACL (Agent Communication Language) – physical connection and responsibility delegation – perception × action – social model A G E N T ' S B O D Y A G E N T ' S W R A P P E R
- 11. Agent is not just a Program • an agent in the context of Distributed Artificial Intelligence is a member of a multi-agent community, where its behaviour and logic behind reasoning has to bee seen from the multi-agent perspective • freely interact, interaction among agents is emergent • can group into coalitions, teams, they can benefit from this • do not have to be benevolent, have free will, can cheat
- 12. • can leave/join the community • can adapt and improve their social role • however there are also other agents such as migrating agent, viruses, information seekers … who are not members of multi-agent community in the above sense
- 13. Agent is not an Object • Objects and Agents both – hide information, allow distributed tractability – provide interaction mechanisms among system components • Objects unlike Agents – are passive, do not operate unless activated – are too grainy structured (ABS: System – Community – Agent) – exchange too primitive imperative message, (agents communicate declarative knowledge) – do not support flexible organisational relationship, the systems object model is fixed – present designed behaviour, while agent are expected to form they own patterns behaviour emergently (from interaction)
- 14. Classification of Agents • Reactive agent - A reactive agent is not much more than an automaton that receives input, processes it and produces an output. • Deliberative agent - A deliberative agent in contrast should have an internal view of its environment and is able to follow its own plans. • Hybrid agent - A hybrid agent is a mixture of reactive and deliberative, that follows its own plans, but also sometimes directly reacts to external events without deliberation.
- 15. Reactive Agents • Reactive agents are agents that do not contain any symbolic knowledge representation (ie: no state, no representation of the environment, no representation of the other agents, ...). Their behaviour is simply defined by a set of perception-action rules. • Subsumption architecture – System of ordered layers of perception-action rules, where the lower layers take precedence. Example of rock sample collecting robots (Steels), inspired by ants: if detect an obstacle then change direction if carrying samples and at the base then drop samples if carrying samples and not at the base then travel up if detect a sample then pick sample up if true then move randomly
- 16. Cognitive Agents • Cognitive Agents are agents with an explicit knowledge representation of own capability, other agents, the environment, etc. • There are various models of agents’ cognitive states (differ in purpose, generality) – BDI (Belief Desire Intention) – Joint Intentions Theory – 3bA (Tri-Base Acquaintance Model)
- 17. Interaction among Agents • Organization – an arrangement of relationships between individuals or components, division of tasks, distribution of roles, and contribution-awards • Cooperation – sharing responsibilities in satisfying shared goal and generating mutually dependent roles in joint activities • Coordination – management of agents activities so that they coordinate their deeds with each other in order to share resources, meet their own interests
- 18. Interaction among Agents cont’ • Negotiation – information exchange aimed at resolving conflict of access to resources, different solutions to the same problem or goal conflicts • Communication – information, knowledge and request exchange via mutually agreed ACL • Benevolence – agent are benevolent if they will agree to cooperate in asked/required
- 20. Agent Communication Language In order to ensure agent interoperability, mutually agreed communication protocol (ACL) must be provided • Mutual knowledge understanding – translating from one knowledge representation language into another – sharing of semantics (and often pragmatics) • Inter Agent Communication – transport protocol (e.g. TCP/IP, SMTP, HTTP, …) – communication language – interaction protocol
- 21. Knowledge Sharing Effort is an initiative addressing agent interoperability and knowledge sharing • Ontolingua - a software tool and methodology for - means for sharing semantics (and often pragmatics) of represented knowledge; • Knowledge Interchange Format (KIF) - an inter- lingua for translations between different knowledge representations;
- 22. • Knowledge Query and Manipulation Language (KQML) - a language for communicating attitudes about the shared knowledge. • Ontologies-KIF-KQML is sometimes denoted as ACL. An ACL message is a KQML expression where arguments are KIF sentences formed from terms from appropriate ontologies. • Foundation for Intelligent Physical Agents Organization (FIPA).
- 23. KQML Message Message Struture: communication layer message layer content layer
- 24. Basic Models of Communication • Broadcasting of a task announcement – autonomous communication – communication intensive • Central Communication Agent – well organized, saves communication – central, fragile, communication bottleneck • Acquaintance Models – model of the environment in an abstract sense
- 25. Broadcasting a Task Announcement Agent 2 Agent 2 Agent 2 Agent 2 Agent 1 Agent 2
- 26. Central Communication Agent Agent 2 Agent 2 Agent 2 Agent 2 FacilitatorAgent 1 Agent 2
- 27. Utilisation of an Acquaintance Model Agent 2 Agent 2 Agent 2 Agent 2 Agent 1 Agent 2
- 28. Applications • DIDABOTS (Didactic Robots) Distributed Intelligence for Smart HomeAppliances
- 29. THANK YOU !!
Editor's Notes
- #4: John McCarthy, who coined the term in 1956,defines it as "the science and engineering of making intelligent machines