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L1. Introduction Motivations Human world physical world humans knowledge reasoning action/behavior communications Agent world computers (virtual space) + the Internet agents knowledge acquisition ? representation ? knowledge base ? AI uncertainty ? reasoning ? action/behavior ? collaborations negotiations communications? collaborations? UI: ubiquitous intelligence negotiations? MA + DAI Contents AI techniques Syllabus and schedule - agents - agent knowledge representation - agent inference and reasoning - agent learning MA &DAI - agent interactions and communications - agent collaborations - agent negotiations - a multi-agents system Ubiquitous Intelligence - context from the real world - intelligence processing and responding to the context Method lecture references 1. “Artificial Intelligence – A Modern Approach”, Stuart Russell and Peter Norvig, Prentice Hall, ISBN 0- 13-103805-2 (English version). 2. “Jess in Auction – Rule-based Systems in Java”, Ernest Friedman-Hill, Manning, ISBN 1-930110-89-8. readings and seminar references writing reports (final report and presentation) focus on one of the papers or systems from the references and write a report that includes your understanding and ideas. Agent - Definitions? American Heritage Dictionary: ”... One that acts or has the power or authority to act ... or represent another” Russel and Norvig: ”An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors.” Maes, Pattie: ”Autonomous Agents are computational systems that inhabit some complex dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed”. Hayes-Roth: ”Intelligent Agents continuously perform three functions: perception of dynamic conditions in the environment; action to affect conditions in the environment; and reasoning to interpret perceptions, solve problems, draw inferences, and determine actions. ...... (what is your definition?) Agent - Properties? Wooldridge and Jennings: An Agent is a piece of hardware or (more commonly) software-based computer system that enjoys the following properties: • Autonomy: agents operate without the direct intervention of humans or others, and have some kind of control over their actions and internal state; • Pro-activeness: agents do not simply act in response to their environment, they are able to exhibit goal-directed behavior by taking the initiative. • Reactivity: agents perceive their environment and respond to it in timely fashion to changes that occur in it. • Social Ability: agents interact with other agents (and possibly humans) via some kind of agent-communication language.” • Mobility: the ability of an agent to move around a network • Rationality: an agent will act in order to achieve its goals and will not act in such a way as to prevent its goals being achieved” Agent? There are many definitions of agents • • Often quite specific Or extremely general In summary, an agent act or behave rationally on behalf another user or entity has some of the above characteristics Many Names Many synonyms of the term ”intelligent agent” » Robots » Software Agents or Softbots » Knowbots » Taskbots » Userbots » ... Related Fields Fields that inspired the Agent field? • • • • Artificial Intelligence - Agent Intelligence, Micro-aspects of Agents Software Engineering - Agent as an abstraction Distributed Systems and Computer Networks - Agent Architectures, Multi-Agent Systems, Coordination Game Theory and Economics - Negotiation • Ubiquitous Intelligence - Agent linked with the real world How to design the agent program • agent = architecture + agent program – The architecture, in general, • makes the percepts from the sensors available to the program, • runs the program, • feeds the program action’s choices to the effectors – architecture may be • a plain computer • a special-purpose hardware • some software – The agent program is a function that implements agent mapping from percepts to actions. It is run on the architecture. percepts in actions out agent program Build an Agent Program Four necessary components of building an agent program: Percepts from the environment Actions toward the goal Goals Clearly defined Agent Action Input Sensor Input Environment Simulating the real world Environment An example: designing an automated taxi driver 自動タクシー運転手 Percepts cameras, speedometer, GPS, sonar Actions steer, accelerate, brake Goals Safely to destination Environment traffic light, other traffic, pedestrians, in Japan Four types of agent program: -Simple reflex agents -Agents that keep track of the world -Goal-based agents -Utility-based agents An example: the vacuum problem 自動掃除機 The vacuum world: 2 squares vacuum 1 3 5 7 2 4 6 8 dirt State(状態): one of the eight states above.(上の8状態が全て) Operators(操作、アクション): move left(左に移動), move right(右に移動), suck(吸取る). start-state(初期状態):Right room has dirt, left room has dirt and vacuum is in left room.(上 の図の1) goal-state(目標状態): no dirt left in any square.(上の図の7または8) An example: find the gold in a Wumpus world 金を自動的に探索機 The wumpus world is a grid of squares surrounded by walls, where each square can contain agents and objects. The agent always starts in the lower left corner, a square that we will label [1,1]. The agent’s task is to find the gold, return to [1,1] and climb out of the cave. 12 13 14 s 4 9 w s g 4 2 b 10 6 7 ok 1 ok 2 b START ok 1 b b A b Breeze g Gold p Pit s Stench 3 p b 3 4 ok 2 Agent 11 p s 0 A ok 5 ok 1 p b 8 3 15 w Wumpus Robots: 本体(からだ)+脳みそ An example: the vacuum problem Vacuuming robot An example: designing an automated taxi driver Taxi robot An example: find the gold in a Wumpus world Gold finding robot Brain: 脳みそ 推理ができる行動ができる MAS &DAI is concerned with • • • • Agent Granularity Heterogenity of Agents Methods of distributing control (among agents) Communication Possibilities MAS – coarse agent granularity and high-level communication MAS is • • • • • To solve problems too large for a centralized agent To allow interconnecting and interoperation of multiple legacy systems To provide a solution to inherently distributed problems To provide solutions where expertise is distributed To offer conceptual clarity and simplicity of design the benefits are • • • • Faster problem solving Decreasing communication Flexibility Increased reliability