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SIF8072 Distributed AI and Intelligent Agents

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Title: SIF8072 Distributed AI and Intelligent Agents


1
SIF8072 Distributed AI and Intelligent Agents
  • Amund Tveit
  • Department of Computer and Information Science
  • Norwegian University of Science and Technology
  • amund.tveit_at_idi.ntnu.no
  • http//www.idi.ntnu.no/amundt/
  • 47 4 162-6572

2
.. But before we start
  • Lets present ourselves
  • What is your name?
  • Why did you choose this course?

3
Lecture Outline
  • Practical Information
  • Motivation
  • What will you learn from this course?
  • What is an agent?
  • What is Distributed Artificial Intelligence?
  • Conclusion of the lecture

4
Practical Information - I
  • All course-related information
  • ? Web-page http//www.idi.ntnu.no/agent/
  • Lectures Thursdays, 1400-1600, aud. F4
  • ? Web-page http//www.idi.ntnu.no/agent/lectur
    es/
  • Exercises Tuesdays, 1700-1900, aud. F4
  • ? Web-page http//www.idi.ntnu.no/agent/exerci
    ses/
  • Exam Date Friday, December 14th
  • ? Web-page http//www.idi.ntnu.no/agent/exam/
    (old exams)

5
Practical Information - II
  • Curriculum
  • Paper collection (17 papers), lecture notes and
    exercises
  • Paper collection can be purchased at the IDI
    department, room 122.
  • Exercises and Project
  • 6 mandatory exercises and one mandatory project
  • Questions regarding Exercises and Project?
  • ? Scientific Assistant Jinghai Rao
  • jinghai_at_idi.ntnu.no, phone (7 35)9-4480, room
    343

6
Motivation
  • Agents, the next paradigm for Software?
  • Agent-Oriented taking over for Object-Oriented?
  • Agents crucial for Open Distributed Systems?
  • Agents the most natural entity in e-commerce?
  • Agent and Peer-to-Peer Technology inseparable?

7
What will you learn from this course?
  • Know what an agent and an agent system is
  • Have a good overview of important agent issues
  • Agent-Oriented Software Engineering
  • Agent Coordination, Negotiation, and
    Communication
  • Micro (intra-Agent) and Macro (agent systems)
    agent architectures
  • Agent Intelligence Mechanisms
  • Get valuable hands-on experience in developing
    agent systems
  • Being able to distinguish hype from golden
    nuggets in the area of Software Agents

8
Lectures
  • 20010830 Subject Overview (today)
  • 20010906 Agent-Oriented Software Engineering
  • 20010913 Coordination in Multi-Agent Systems
    (MAS)
  • 20010920 Negotiation in MAS - I
  • 20010927 Negotiation in MAS II
  • 20011004 Agent Communication Languages I
    (knowledge rep.)
  • 20011011 Agent Communication Languages II
    (FIPA, KQML, ..)
  • 20011018 Architectures of MAS
  • 20011025 Agent Theories
  • 20011101 Agent Architectures (agent internals)
  • 20011108 Classifications of Agents
  • 20011115 Agent-Mediated Electronic Commerce
  • 20011122 Summary of the Course

9
What is an Agent?
  • 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
  • There are many definitions of agents
  • Often quite narrow
  • Or extremely general

10
Agent - General 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.

11
Agent - More Specific Definitions
  • Smith, Cypher and Spohrer
  • Let us define an agent as a persistent software
    entity dedicated to a specific purpose.
    Persistent distinguishes agents from
    subroutines agents have their own ideas about
    how to accomplish tasks, their own agendas.
    Special purpose distinguishes them from
    multifunction applications agents are typically
    much smaller.
  • 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.

12
Agent - Industrial Definition
  • IBM
  • Intelligent agents are software entities that
    carry out some set of operations on behalf of a
    user or another program with some degree of
    independence or autonomy, and in doing so, employ
    some knowledge or representations of the users
    goals or desires

13
Weak Notion of Agency
  • 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.

14
Strong Notion of Agency
  • Wooldridge and Jennings
  • Weak Notion in addition to
  • Mobility the ability of an agent to move around
    a network
  • Veracity agent will not knowingly communicate
    false information
  • Benevolence agents do not have conflicting goals
    and always try to do what is asked of it.
  • 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

15
Summary of Agent definitions
  • An agent act on behalf another user or entity
  • An agent has the weak agent characteristics
    (autonomy, pro-activity, reactivity and social
    ability)
  • An agent may have the strong agent
    characteristics (mobility, veracity, benevolence
    and rationality)

16
.. Dear child gets many names
  • Many synonyms of the term intelligent agent
  • Robots
  • Software Agents or Softbots
  • Knowbots
  • Taskbots
  • Userbots
  • ...

17
Why the buzz around agents?
  • Lack of programming paradigm for distributed
    systems
  • Tries to meet problems of closed world
    assumption in object-orientation
  • Agent is a frequently used term to describe
    software in general (due to vague definitions)
  • Massive Media Hype in the era of dot-coms.

18
Autonomy is a key feature
  • Examples
  • Thermostat
  • Control/Regulator
  • Software Daemon
  • Printer Server
  • Web/HTTP Server

19
Example of Agents
20
Distributed Artificial Intelligence (DAI)
  • DAI is a sub-field of AI
  • DAI is concerned with problem solving where
    agents solve (sub-) tasks (macro level)
  • Main areas of DAI
  • Multi-Agent Systems (MAS)
  • Distributed Problem Solving (DPS)

21
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

22
DAI is not concerned with..
  • Issues of coordination of concurrent processes at
    the problem solving and representational level
  • Parallel Computer Architectures, Parallel
    Programming Languages or Distributed Operating
    Systems
  • No semaphors, monitors, threads etc.

23
Motivation behind MAS
  • 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

24
Benefits of MAS
  • Faster problem solving
  • Decreasing communication
  • Flexibility
  • Increased reliability

25
Heterogeneity Degrees in MAS
  • Low identical agents, different resources
  • Medium- different agent expertise
  • High share only interaction protocol (e.g. FIPA
    or KQML)

26
Cooperative and Self-interested MAS
  • Cooperative
  • Agents designed by interdependent designers
  • Agents act for increased good of the system (i.e.
    MAS)
  • Concerned with increasing the systems performance
    and not the individual agents
  • Self-interested
  • Agents designed by independent designer
  • Agents have their own agenda and motivation
  • Concerned with the benefit of each agent
    (individualistic)
  • The latter more realistic in an Internet-setting?

27
Distributed AI Perspectives
28
Conclusions of Lecture
  • DAI is part of AI
  • MAS is a part of DAI
  • MAS macro issues of agent systems
  • Intelligent Agents micro issues
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