1. L1. Introduction

2. Motivations Human world physical world humans knowledge reasoning action/behavior communications collaborations negotiations Agent world computers (virtual space) + the Internet agents knowledge acquisition ? representation ? knowledge base ? uncertainty ? reasoning ? action/behavior ? communications? collaborations? negotiations? AI MA + DAI

3. Contents AI techniques - agents - agent knowledge representation - agent inference and reasoning - agent learning MA ＆ DAI - agent interactions and communications - agent collaborations - agent negotiations - a multi-agents system Syllabus and schedule

4. Method lecture references 1. “Artificial Intelligence – A Modern Approach”, Stuart Russell and Peter Norvig, Prentice Hall, ISBN 0- 13-103805-2 (English version). 2. “Multiagent Systems-A Modern Approach to Distributed Artificial Intelligence”, edited by Gerhard Weiss, The MIT Press, ISBN 0-262-23203-0, 1999. 3. “Multi-Agent Systems – An Introduction to Distributed Artificial Intelligence”, Jacques Ferber, Addison Wesley, ISBN 0-201-36048-9, 1999. 4. “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.

5. 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?)

6. 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”

7. 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

8. Many Names Many synonyms of the term ”intelligent agent” »Robots »Software Agents or Softbots »Knowbots »Taskbots »Userbots »...

9. 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

10. 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. agent program percepts in actions out

11. Build an Agent Program Percepts Actions Goals Environment Simulating the real world toward the goal Clearly defined from the environment Four necessary components of building an agent program:

12. An example: designing an automated taxi driver 自動タクシー運転手 Percepts Actions Goals Environmenttraffic light, other traffic, pedestrians, in Japan steer, accelerate, brake Safely to destination cameras, speedometer, GPS, sonar Four types of agent program: -Simple reflex agents -Agents that keep track of the world -Goal-based agents -Utility-based agents

13. The vacuum world: 2 squares 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 ） vacuum dirt 2468 1357 An example: ｔ he vacuum problem 自動掃除機

14. 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. 1 2 3 4 4 3 2 1 s p START g g w b A p p b b b b b s s A b g g p s w Agent Breeze Gold Pit Stench Wumpus ok ０ 123 4567 891011 13121415 An example: find the gold in a Wumpus world 金を自動的に探索機

15. Robots ： 本体（からだ）＋脳み そ An example: ｔ he vacuum problem  Vacuuming robot An example: designing an automated taxi driver  Vacuuming 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

19. Agent Granularity Heterogenity of Agents Methods of distributing control (among agents) Communication Possibilities  MAS – coarse agent granularity and high-level communication MAS &DAI is concerned with

20. 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 MAS is Faster problem solving Decreasing communication Flexibility Increased reliability the benefits are