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©Intelligent Agent Technology and Application, 2006, Ai Lab NJU
Intelligent Agent
Technology and Application
Course overview
and
what is intelligent agent
Sept. 2006©Gao Yang, Ai Lab NJU2
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 Agent
?
Sept. 2006©Gao Yang, Ai Lab NJU3
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”.
Sept. 2006©Gao Yang, Ai Lab NJU4
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.
Sept. 2006©Gao Yang, Ai Lab NJU5
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”
Sept. 2006©Gao Yang, Ai Lab NJU6
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.”
Sept. 2006©Gao Yang, Ai Lab NJU7
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
Sept. 2006©Gao Yang, Ai Lab NJU8
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)
Sept. 2006©Gao Yang, Ai Lab NJU9
Dear child gets many names…
Many synonyms of the term “Intelligent agent”
– Robots
– Software agent or softbots
– Knowbots
– Taskbots
– Userbots
– ……
Sept. 2006©Gao Yang, Ai Lab NJU10
Autonomy is the key feature of agent
Examples
– Thermostat
Control / Regulator
Any control system
– Software Daemon
Print server
Http server
Most software daemons
Agent
Envi ronment
Act i onI nput
SensorI nput
Sept. 2006©Gao Yang, Ai Lab NJU11
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
Sept. 2006©Gao Yang, Ai Lab NJU12
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.
Sept. 2006©Gao Yang, Ai Lab NJU13
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.
Sept. 2006©Gao Yang, Ai Lab NJU14
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.
Sept. 2006©Gao Yang, Ai Lab NJU15
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.
Sept. 2006©Gao Yang, Ai Lab NJU16
Discrete vs. continuous
Discrete vs. continuous– An environment is discrete if there are a fixed,
finite number of actions and percepts in it.
Sept. 2006©Gao Yang, Ai Lab NJU17
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.
Sept. 2006©Gao Yang, Ai Lab NJU18
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:
Sept. 2006©Gao Yang, Ai Lab NJU19
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).
Sept. 2006©Gao Yang, Ai Lab NJU20
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
Sept. 2006©Gao Yang, Ai Lab NJU21
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.
Sept. 2006©Gao Yang, Ai Lab NJU22
Social ability
Social ability– Must negotiate and cooperate with others.
Sept. 2006©Gao Yang, Ai Lab NJU23
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.
Sept. 2006©Gao Yang, Ai Lab NJU24
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.Thinking it.
Sept. 2006©Gao Yang, Ai Lab NJU25
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
Sept. 2006©Gao Yang, Ai Lab NJU26
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.
Sept. 2006©Gao Yang, Ai Lab NJU27
Example of agents
MobileCustomer
Agent(Peer)
Agent(Peer)
Agent(Peer)
Agent(Peer)
M obileC ustom er
M obileC ustom er
M obileC ustom er
Sept. 2006©Gao Yang, Ai Lab NJU28
Distributed Artificial Intelligence (DAI)
DAI is a sub-field of AI
DAI is concerned with problem solving where agent
s 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)
Some historiesSome histories
Sept. 2006©Gao Yang, Ai Lab NJU29
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
Di st r i but edComput i ng
Ar t i fi ci alI nt el l i gence
Di st r i but edAI
Mul t i - AgentSyst ems
Di st r i but edProbl emSol vi ng
Sept. 2006©Gao Yang, Ai Lab NJU30
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)
Sept. 2006©Gao Yang, Ai Lab NJU31
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
Sept. 2006©Gao Yang, Ai Lab NJU32
Benefits of MAS
Faster problem solving
Decreasing communication
– Higher semantics of communication (speech-act
level)
Flexibility
Increasing reliability
Sept. 2006©Gao Yang, Ai Lab NJU33
Heterogeneity degrees in MAS
Low
– Identical agents, different resources
Medium
– Different agent expertise
High
– Share only interaction protocol (e.g. FIPA or
KQML)
Sept. 2006©Gao Yang, Ai Lab NJU34
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?
Sept. 2006©Gao Yang, Ai Lab NJU35
Our categories about MAS
Cooperation
– Both has a common object
Competitive
– Each have different objects which are contradicto
ry.
Semi-competitive
– Each have different objects which are conflictive,
but the total system has one explicit (or implicit)
objectThe first now is known as TEAMWORK.
Sept. 2006©Gao Yang, Ai Lab NJU36
Distributed AI perspectives
Perspecti ves
Agent
Grou
p
Desi gnerSpeci fi c Approaches
Cooperati on
Coordi nat i on
Nego
tiat
i on
CoherentBehavi or
Pl anni ng
Di st r i but edAI
Met hodsAna
l ysi s
Desi gn
Tool s
Appl
i cat
i ons
Testbeds
Archi tecture
Reactive
Del iberative
Hybrid
TheoryLanguage
Sept. 2006©Gao Yang, Ai Lab NJU37
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
…..
Sept. 2006©Gao Yang, Ai Lab NJU38
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
Sept. 2006©Gao Yang, Ai Lab NJU39
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.
Sept. 2006©Gao Yang, Ai Lab NJU40
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.
Sept. 2006©Gao Yang, Ai Lab NJU41
Coursework
3. Please determine the environment’s type.
Chess Poker Mine-sweeper
E-shopping
Accessible??
Deterministic??
Episodic??
Static??
Discrete??