The document discusses intelligent agents and related concepts. It begins by defining agents based on general definitions from dictionaries and literature. It then provides more specific definitions of intelligent agents from various sources. The document distinguishes between weak and strong notions of agency. It also compares and contrasts agents with objects and expert systems. The document discusses different types of environments that agents can operate in and how this impacts agent design. It concludes by discussing distributed artificial intelligence and multi-agent systems at a high level.

1. Intelligent Agent
Technology and Application

2. What is intelligent agent
 Field that inspired the agent fields?
 Artificial Intelligence
 Agent intelligence and micro-agent
 Software Engineering
 Agent as an abstracted entity
 Distributed System and Computer Network
 Agent architecture, MAS, Coordination
 Game Theory and Economics
 Agent Negotiation
 There are two kinds definition of agent
 Often quite narrow
 Extremely general
2
Agent
?

3. 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, Parrie
 ”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”.
3

4. 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.
4

5. Agent: industrial definitions
 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 user’s goals or desires”
5

6. Agent: weak notions
 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.”
6

7. Agent: strong notions
 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
7

8. Summary of agent definitions
 An agent act on behalf user or another entity.
 An agent has the weak agent characteristics. (Autonomy, Pro-activeness,
Reactivity, Social ability)
 An agent may have the strong agent characteristics. (Mobility, Veracity,
Benevolence, Rationality)
8

9. Dear child gets many names…
 Many synonyms of the term “Intelligent agent”
 Robots
 Software agent or softbots
 Knowbots
 Taskbots
 Userbots
 ……
9

10. Autonomy is the key feature of agent
 Examples
 Thermostat
 Control / Regulator
 Any control system
 Software Daemon
 Print server
 Http server
 Most software daemons
Agent
Environment
Action
Input
Sensor
Input
1
0

11. Type of environment
 An agent will not have complete control over its environment, but have
partial control, in that it can influence it.
 Scientific computing or MIS in traditonal computing.
 Classification of environment properties [Russell 1995, p49]
 Accessible vs. inaccessible
 Deterministic vs. non-deterministic
 Episodic vs. non-episodic
 Static vs. dynamic
 Discrete vs. continuous
11

12. Accessible vs. inaccessible
 Accessible vs. inaccessible
 An accessible environment is one in which the agent can
obtain complete, accurate, up-to-date information about the
environment’s state. (also complete observable vs. partial
observable)
 Accessible: sensor give complete state of the environment.
 In an accessible environment, agent needn’t keep track of
the world through its internal state.
12

13. Deterministic vs. non-deterministic
 Deterministic vs. non-deterministic
 A deterministic environment is one in which any action has a
single guaranteed effect , there is no uncertainty about the
state that will result from performing an action.
 That is, next state of the environment is completely
determined by the current state and the action select by the
agent.
 Non-deterministic: a probabilistic model could be available.
13

14. Episodic vs. non-episodic
 Episodic vs. non-episodic
 In an episodic environment, the performance of an agent is
dependent on a number of discrete episodes, with no link
between the performance of an agent in different scenarios.
It need not reason about the interaction between this and
future episodes. (such as a game of chess)
 In an episodic environment, agent doesn’t need to
remember the past, and doesn’t have to think the next
episodic ahead.
14

15. Static vs. dynamic
 Static vs. dynamic
 A static environment is one that can assumed to remain
unchanged expect by the performance of actions by the
agents.
 A dynamic environment is one that has other processes
operating on it which hence changes in ways beyond the
agent’s control.
15

16. Discrete vs. continuous
 Discrete vs. continuous
 An environment is discrete if there are a fixed, finite number
of actions and percepts in it.
16

17. Why classify environments
 The type of environment largely determines the design of
agent.
 Classifying environment can help guide the agent’s
design process (like system analysis in software
engineering).
 Most complex general class of environments
 Are inaccessible, non-deterministic, non-episodic, dynamic,
and continuous.
17

18. Discuss about environment: Gripper
 Gripper is a standard example for probabilistic planning
model
 Robot has three possible actions: paint (P), dry (W) and
pickup (U)
 State has four binary features: block painted, gripper dry,
holding block, gripper clean
 Initial state:
 Goal state:
18

19. Intelligent agent vs. agent
 An intelligent agent is one that is capable of flexible
autonomous action in order to meet its design objectives,
where flexibility means three things:
 Pro-activeness: the ability of exhibit goal-directed behavior
by taking the initiative.
 Reactivity: the ability of percept the environment, and
respond in a timely fashion to changes that occur in it.
 Social ability: the ability of interaction with other agents
(include human).
19

20. Pro-activeness
 Pro-activeness
 In functional system, apply pre-condition and post-condition
to realize goal directed behavior.
 But for non-functional system (dynamic system), goal must
remain valid at least until the action complete.
 agent blindly executing a procedure without regard to
whether the assumptions underpinning the procedure are
valid is a poor strategy.
 Observe incompletely
 Environment is non-deterministic
 Other agent can affect the environment
20

21. Reactivity
 Reactivity
 Agent must be responsive to events that occur in its
environment.
 Building a system that achieves an effective balance between
goal-directed and reactive behavior is hard.
21

22. Social ability
 Social ability
Must negotiate and cooperate
with others.
22

23. Agent vs. object
 Object
 Are defined as computational entities that encapsulate some
state, are able to perform actions, or methods on this state,
and communicate by message passing.
 Are computational entities.
 Encapsulate some internal state.
 Are able to perform actions, or methods, to change this state.
 Communicate by message passing.
23

24. Agent and object
 Differences between agent and object
 An object can be thought of as exhibiting autonomy over its
state: it has control over it. But an object does not exhibit
control over it’s behavior.
 Other objects invoke their public method. Agent can only
request other agents to perform actions.
 “Objects do it for free, agents do it for money.”
 (implement agents using object-oriented
technology)……Thinking it.
24

25. Agent and object
 In standard object model has nothing whatsoever to say
about how to build systems that integrate reactive, pro-
active, social behavior.
 Each has their own thread of control. In the standard object
model, there is a single thread of control in the system.
 (agent is similar with an active object.)
 Summary,
 Agent embody stronger notion of autonomy than object
 Agent are capable of flexible behavior
 Multi-agent system is inherently multi-threaded
25

26. Agent and expert system
 Expert system
 Is one that is capable of solving problems or giving advice in
some knowledge-rich domain.
 The most important distinction
 Expert system is disembodied, rather than being situated.
 It do not interact with any environment. Give feedback or
advice to a third part.
 Are not required to interact with other agents.
26

27. Example of agents
Mobile
Customer
Agent
(Peer)
Agent
(Peer)
Agent
(Peer)
Agent
(Peer)
Mobile
Customer
Mobile
Customer
Mobile
Customer
2
7

28. 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 area of DAI
 Distributed problem solving (DPS)
 Centralized Control and Distributed Data (Massively Parallel Processing)
 Multi-agent system (MAS)
 Distributed Control and Distributed Data (coordination crucial)
28
Some histories

29. DAI is concerned with……
 Agent granularity (agent size)
 Heterogeneity agent (agent type)
 Methods of distributing control (among agents)
 Communication possibilities
 MAS
 Coarse agent granularity
 And high-level communication
29
Distributed
Computing
Artificial
Intelligence
Distributed
AI
Multi-Agent
Systems
Distributed
Problem
Solving

30. DAI is not concerned with……
 Issues of coordination of concurrent processes at the
problem solving and representational level.
 Parallel computer architecture, parallel programming
languages or distributed operation system.
 No semaphores, monitors or threads etc.
 Higher semantics of communication (speech-act level)
30

31. Motivation behind MAS
 To solve problems too large for a centralized agent
 E.g. Financial system
 To allow interconnection and interoperation of multiple
legacy system
 E.g. Web crawling
 To provide a solution to inherently distributed system
 To provide a solution where expertise is distributed
 To provide conceptual clarity and simplicity of design
31

32. Benefits of MAS
 Faster problem solving
 Decreasing communication
 Higher semantics of communication (speech-act level)
 Flexibility
 Increasing reliability
32

33. Heterogeneity degrees in MAS
 Low
 Identical agents, different resources
 Medium
 Different agent expertise
 High
 Share only interaction protocol (e.g. FIPA or KQML)
33

34. 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?
34

35. Our categories about MAS
 Cooperation
 Both has a common object
 Competitive
 Each have different objects which are contradictory.
 Semi-competitive
 Each have different objects which are conflictive, but the
total system has one explicit (or implicit) object
The first now is known as TEAMWORK.
35

36. Distributed AI perspectives
Perspectives
Agent
G
r
o
u
p
D
e
s
i
g
n
e
r
Specific Approaches
Cooperation
Coordination
Negotiation
Coherent
Behavior
Pl
an
ni
ng
Distributed
AI
Methods
Analysis
Design
T
o
o
l
s
Applications
Testbeds
Architecture
Reactive
Deliberative
Hybrid
Theory
L
a
n
g
u
a
g
e
3
6

37. Our Thinking in MAS
 Single benefit vs. collective benefit
 No need central control
 Social intelligence vs. single intelligence
 Self-organize system
 Self-form, self-evolve
 Intelligence is emergence, not innative
 …..
37

38. Conclusions of lecture
 Agent has general definition, weak definition and strong
definition
 Classification of the environment
 Differences between agent and intelligent agent, agent
and object, agent and expert system
 Multi-agent system is macro issues of agent systems
38

39. Coursework
 1. Give other examples of agents (not necessarily
intelligent) that you know of. For each, define as precisely
as possible:
 (a). the environment that the agent occupies, the states that
this environment can be in, and the type of environment.
 (b). The action repertoire available to the agent, and any pre-
conditions associated with these actions;
 (c). The goal, or design objectives of the agent – what it is
intended to achieve.
39

40. Coursework
 2. If a traffic light (together with its control system) is
considered as intelligent agent, which of agent’s
properties should be employ? Illustrate your answer by
examples.
40

41. Coursework
 3. Please determine the environment’s type.
41
Chess Poker Mine-
sweeper
E-
shopping
Accessible??
Deterministic
??
Episodic??
Static??
Discrete??