lecture 1 introduction to intelligent agents

Multi-agent Engineering

Lecture 1Introduction to Intelligent Agents

Ivan Tanev

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Ivan TanevRoom KC-223, Department of Information Systems Design,Faculty of Sciences and Engineering, Doshisha University

E-mail: itanev@mail.doshisha.ac.jpWeb page: http://isd-si.doshisha.ac.jp/itanev/

Contact Information

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Multiagent Systems: a Modern Approach to Distributed Artificial Intelligence (First edition)#Pages: 619Year of Publication: 1999 ISBN:0-262-23203-0 Editor: Gerhard Weiss

Technical Univ. of Munich, Munich, Germany Publisher: MIT Press Cambridge, MA, USA

Textbooks

An Introduction to Multiagent Systems#Pages: 366Year of Publication: 2002ISBN: 0-471-49691-XEditor: Michael WooldridgePublisher: Wiley Publishers 3

Multiagent Systems (Second edition)#Pages: 920Year of Publication: 2016ISBN:0-262-23203-0 Editor: Gerhard Weiss

Technical Univ. of Munich, Munich, Germany Publisher: MIT Press Cambridge, MA, USA

• Midterm report (50 points max), and• End-term report (50 points max).

Evaluation

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• Make a memo of new keywords for each lecture.• You may need a dictionary during the lectures.

Vocabulary

URL, Evaluation, etc.URL of Lecture materials: PPT slides (as pdf) + Video

of the lecture (mp4):http://isd-si.doshisha.ac.jp/itanev/lectures/MAS/

1. Introduction

2. What are agents

3. What is not an agent

4. Examples of single agents

5. Multiagent systems

6. Examples of multiagent systems

Outline

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• Computers are not very good at knowing what to do.• We require systems that can decide for themselves what

they need to do in order to satisfy their design objectives.

• Such computer systems are known as agents.• Agents must operate robustly in changing,

unpredictable environments – intelligent agents(learning and adaptation), autonomous agents (capable of modifying the way in which they achieve their objectives )

1. Introduction

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A computer system that is situated in some environment

(, and is capable of autonomous actions in this

environment in order to meet its design objectives).

2. What are Agents

Agent

Environment

Sensorinput

Action output

First (simple) example: air-conditioner7

The key problem facing an agent is that of decision-

making process - deciding which of its actions it

should perform in order to satisfy its design objectives.

2. What are Agents

Agent

Environment

Sensorinput

Action output

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Environment: Agents can occupy many different types of

environment.

Classification of environment properties:• Accessible vs. inaccessible,• Deterministic vs. non-deterministic,• Static vs. dynamic• Discrete vs. continuous

2. What are Agents

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Autonomy: Ability to act without the intervention of humans or other systems: they have control both over their own internal state and over their behavior.

Intelligent Agent?Definition of Intelligence:David Wechsler: "... the aggregate or global capacity of the individual

to act purposefully, to think rationally, and to deal effectively with his environment."

Cyril Burt: "...innate general cognitive ability." Herrnstein and Murray: "...cognitive ability." Sternberg and Salter: "...goal-directed adaptive behavior.“…Intelligent Agent: Agent, which is capable of flexible autonomous

action in order to meet its design objectives.

2. What are Agents

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Intelligent Agent: Agent, which is capable of flexible autonomous action in order to meet its design objectives.

Flexibility:

• Reactivity – ability to perceive the environment and to respond in a timely fashion to changes that occur in it in order to satisfy their design objectives,

• Pro-activeness – ability to exhibit goal-directed behavior by taking the initiative in order to satisfy their design objectives,

• Social ability – capability to interact with other agents (and possibly humans) in order to satisfy their design objectives

2. What are Agents

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Agents are not an arbitrary programs. Agents are…• …reacting to the environment, • …autonomous, • …goal-oriented, and • …persistent.

Agent are not software objects. Agents…• …are more autonomous than objects, • …have flexible behavior: reactive, proactive, social. • …have at least one thread of control but may have more.

3. What is not an Agent

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(1) Any control system with a feedback (“Sensor input”)Space probes, fly-by-wire aircraft, engine control systems, Anti-lock Brake System (ABS), nuclear reactor controlsystems, etc.All these have sensory input, action output, and decision mechanism

4. Examples

Mercedes Benz W116 S-Class without (left) and with (right) ABSSource: http://www.daimler.com/ 13

Thermostat of the air-conditionersensory input: too hot action output: conditioner ONsensory input: too cold action output: conditioner OFF

Software daemons:

E-mail clientsensory input: new e-mail in inbox action output: notify user

Auto doorsensory input: human in front of the door action output: open the door

4. Examples

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3. ExamplesDriving agent – driving a car (“auto-pilot”) in Drive Simulator.YouTube video: https://www.youtube.com/watch?v=Tl73a96e27U

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• The environments contains other agents,

• The agents interact (cooperate) with each other,

• Agents in multiagent system are able to achieve

goals which cannot be achieved by single agent.

4. Multiagent Systems (MAS)

Agent

Environment

Sensorinput

Action output

Agent Agent…

Agent

Agent Agent

Environment

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4. MAS

•Agent = computer system•MAS = distributed system of interacting agents

Agent

Agent Agent

Environment

Why interacting agents: to enable them to cooperate in solving problems,to share expertise, to work in parallel on common problems, to be fault-tolerant through redundancy, to represent multiple viewpoints andthe knowledge of multiple experts, and to be reusable.

Agents features:

Number: from 2 upward,•Uniformity: homogeneous…heterogeneous,•Goals: Contradicting…complementary,•Architecture: Reactive…deliberative,•Abilities (sensors, effectors, cognition):

simple…advanced.

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4. MAS

Characteristics of Multiagent Environments

• They provide an infrastructure specifying communication and interaction protocols,

• They have no centralized designer,• They contain agents that are autonomous, distributed, and

cooperative agents.

Agent

Agent Agent

Environment

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4. MASWhy interested in MAS, instead of a single centralized system? What is the motivation for developing MAS:

To design and manage large and complex information systems.

These include:

• Geographically distributed systems,• System comprising many components,• Systems with huge contents of data,• Broad scope (major portion of a significant domain)

Major approaches to deal with such large and complex systems:modularity, distribution, abstraction, and intelligence.

The use of distributed intelligent modules combines all these 4 approaches yielding a distributed artificial intelligence (DAI). 19

4. MASWhy interested in MAS, instead of a single centralized system? What is the motivation for developing MAS:

To design and manage large and complex information systems.

MAS can solve problems that a single agent cannot solve!

This is the significant difference between MAS and multi-processor, multi-core computers, and computer networks (e.g., of workstations).

In MAS, there is a philosophical transition from quantity (i.e., the number of agents in MAS) into quality (i.e., ability to solve the problem). With few agents, MAS could be unable to solve a given problem, while with enough number of agent MAS could solve the problem.

“The whole is greater than the sum of its parts.” Aristotle

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5. Examples of MAS

The problem comprises 4 predator agents whose goals are to capture a prey by surrounding it on all sides in a world.

Predator-prey Pursuit Problem

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5. Examples of MAS• Airliners equipped with Traffic Allert and Collision Avoidance System (TACAS)(agents = airplanes, equipped with TACAS);

•Electronic commerce and electronic markets, where “buyer” and “seller” agents purchase and sell goods on behalf of their users (agents = sellers, buyers);

• Wikipedia - network of individuals working together. • Through communication among each other, these agents are

able to approach a consensus on content for each topic. • The cooperation among different agents allows for far faster

updates with more depth in topics than would be possible with conventional processes (agents = authors of articles).

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5. Examples of MAS•Real-time management of telecommunication networks, where agents are responsible, e.g., for call forwarding and signal switching and transmission (agents = switches, routers, etc.);

•Optimizing the industrial manufacturing and production processes like supply chain management (agents = different workcells);

• Electronic entertainment and computer games (agents = animated artifacts);

• Analysis of business processes within or between enterprises(agents = people);

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5. Examples of MAS• Modeling and optimization of in-town, national-, or world-wide

transportation systems (agents = vehicles);

This MAS models the overall travel time and fuel consumption ofcars as a function of the way they accelerate on green traffic light. 24

5. Examples of MAS• Modeling and optimization of in-town, national-, or world-wide

transportation systems (agents = vehicles);

This MAS system consist of a three agents: RC car and two virtualmobile obstacles (shown as two red spots). The system models variousobstacle avoidance mechanisms.YouTube video: https://www.youtube.com/watch?v=8W-mV6C8LuQ

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The End

(Q&A)

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