1. Software Agent Technology
Introduction to Technology
Heimo Laamanen

2. Introduction to Technology
Copyright © 2006 by OSCu
The use of this course material is allowed only to private persons for studying purposes. The use of this material for other purposes is prohibited without written permission from the author.
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Definitions of
Software Agents
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Little bit history.
Mid 50’s first ideas about software agents.
John McCarthy, Oliver G. Selfridge.
Research on software agents can be split into two main strands:
first one about mid 70’s and
second one at the beginning of 90’s.
Today software agents are still a hot, diverse topic.
distributed artificial intelligence, robotics, artificial life, distributed object computing, human-computer interaction, intelligent and adaptive interfaces, intelligent search and filtering, information retrieval, etc.
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Agents
Many definitions:
"An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors.”
"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."
"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 so doing, employ some knowledge or representation of the user's goals or desires."
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Agents
Many definitions:
"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 entire multifunction applications; agents are typically much smaller."
An autonomous agent is a system situated within and a part of an environment that senses that environment and acts on it, over time, in pursuit of its own agenda and so as to effect what it senses in the future.
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7. Artificial Life Agents
Introduction
Categories of agents (just one of them)
Autonomous Agents
Biological Agents
Computational Agents
Robotic Agents
Artificial Life Agents
Software Agents
Task-specific Agents
Entertainment Agents
Viruses
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Categories of agents (just another one of them).
Mobile agents
Interface agents
Collaborative agents
Information agents
Reactive agents
Hybrid agents
Heterogeneous agents
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What is an Agent?
Attributes
Autonomy
Proactive
Mobile
Goal-oriented
Temporal continuity
Adaptivity
Communicative
Collaborative
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Agents do it
for money ...
Objects do it,
because
they are commanded to ...
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Agents' Architectures
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Agent Architectures
Maes:
An architecture proposes a particular methodology for building an autonomous agent. It specifies how the overall problem can be decomposed into subproblems, i.e. how the construction of the agent can be decomposed into the construction of a set of component modules and how these modules should be made to interact. The total set of modules and their interactions has to provide an answer to the question of how the sensor data and the current internal state of the agent determine the actions (effector outputs) and future internal state of the agent. An architecture encompasses techniques and algorithms that support this methodology.
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Agent Architectures
Reactive agent
Deliberative agent
Hybrid agent
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Reactive Agent
Reactive agents have
at most a very simple internal representation of the world,
but provide tight coupling of perception and action
Behaviour-based paradigm
Intelligence is a product of interaction between an agent and its environment.
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Reactive Agent
Each behaviour continually maps perceptual input to action output
Reactive behaviour:
action: S -> A
where S denotes the states of the environment, and A the primitive actions the agent is capable of perform.
Example:
action(s) =
Heater off, if temperature OK
Heater on, otherwise
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Reactive Agent S e n s o r E f c t
Agent
Stimulus-response behaviours
State1
State2
Staten
Action1
Action2
Actionn .
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Deliberative Agent
Explicit symbolic model of the world in which decisions are made via logical reasoning, based on pattern matching and symbolic manipulation
sense-plan-act problem-solving paradigm of classical AI planning systems
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Deliberative Agent S e n s o r E f c t
World
Model
Planner
Plan
executor
Agent
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Agents (intelligent).
Internal architecture:
Agents maintain an internal representation of their world.
There is an explicit mental state
Can be modified by some form of symbolic reasoning.
BDI agents
Belief
Desire
Intentions
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Agent – a BDI model
goals
reactions
local goals
co-operative goals
planning and scheduling
options
intentions
goal activation
situations
routine/emergency
situations
local planning
co-operating
selection
execution
joint plans
local plans
perception
situation recognition
POB
ACTION
Belief
generation
world model
mental model
social model
beliefs
Environment
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Hybrid Agent
Combination of deliberative and reactive behaviour
An agent consists of several subsystems
Subsystems that develop plans and make decisions using symbolic reasoning (deliberative component)
Reactive subsystems that are able to react quickly to events without complex reasoning (reactive component)
Layered architectures
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Hybrid Agent
Agent S e n s o r E f c t
Reactive component
State1
State2
Staten
Action2
Actionn .
World
Model
Planner
Plan
executor
Deliberative component
Action1
observations
modifications
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Environment of
Software Agents
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Environment of Software agents
Agents
Communications
Ontology
Platforms
Applications
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Platform
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Platform
A place where agents live
not always needed
Agent management
creation – termination
security
Agent communication services
Agent directory services
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Example
Agent Platform
Agent
Management
System
Directory
Facilitator
Agent Communication Channel
Baseline Protocol
Software
F I P A
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Platforms - FIPA Implementations
Jade (TiLab - Telecom Italy)
LEAP (EU -project)
BlueJade (HP)
FIPA-OS (Nortel - Emorphia)
MicroFIPA-OS (HY)
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Questions?
Comments.
Objections!
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Communications
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Agent Communications
Modelled at higher abstraction level than ’traditional’ data communications.
Speech Act theory
J.R. Searle (1969) Speech Acts
Classic example:
President says ”I declare war” is actually an action.
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Speech acts
Pragmatic theory of language dealing with the syntax and semantics
ACL message types usually modeled after speech acts
Locutionary act
uttering a syntactically acceptable sentence
Illocutionary act (speech act)
communicating of the speakers’ intentions to the hearer
Perlocutionary act
bringing about of the effect on the hearer of the utterance
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Agent Communications
Knowledge sharing
Agent Communication Language
Content Language
Interaction Protocols
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Agent Communications
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Agent Communication Language (ACL)
KQML (Knowledge Query and Manipulation Language)
FIPA-ACL (Agent Communication Language)
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Agent Communication Language
FIPA-ACL
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Communicative acts
Propagate
Propose
Proxy
Query-if
Query-ref
Refuse
Reject-proposal
Request
Request-when
Request-whenever
Subscribe
Accept-proposal
Agree
Cancel
Cfp
Confirm
Disconfirm
Failure
Inform
Inform-if (macro act)
Inform-ref (macro act)
Not-understood
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Communicative Acts (cf. Dr. Poslad’s Lecture)
The door is open?
Open the door (for me)
OK! I’ll open the door
The door is open
I am unable to open the door
I will not open the door
Say when the door becomes open
Anyone want to open the door?
I can open the door for you..at a price
Door? What’s that? Don’t understand...
query
request
agree
inform
failure
refuse
subscribe
cfp
propose
not-understood
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Parameters
:sender
:receiver
:content
:reply-with
:reply-by
:in-reply-to
:envelope
:language
:ontology
:protocol
:conversation-id
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Example of request
The sender requests the receiver to perform some action.
(request :sender i :receiver j :content "open \"db.txt\" for input" :language vb)
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Semantic of Request
<i, request( j, a )> FP: FP(a) [i\j]  Bi Agent( j, a )  Bi PGj Done(a) RE: Done(a)
i = sender, j = receiver, a = action to perform
Done(a) = action performed
Bi Agent( j, a ) = agent i believes that j can perform a
Bi PGj Done(a) = i does not believe that Done(a) is j ’s persistent goal
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Content languages
FIPA-SL
subsets SL0, SL1, and SL2
FIPA-RDF0
RDF based content language
FIPA-CCL
Constraint Choice Language
KIF & FIPA-KIF
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Content languages
KIF (Knowledge Interchange Format)
(salary widgets 72000)
(salary grommets 36000)
(salary fidgets 42000)
(> (* (width chip1) (length chip1)) (* (width chip2) (length chip2)))
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Content languages
FIPA-SL
Formal language used to define the semantics of the FIPA ACL
Logical propositions are expressed in a logic of mental attitudes and actions, formalised in a first order modal language with identity
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Content language
FIPA-SL example
(query-ref
:sender (agent-identifier :name B)
:receiver (set (agent-identifier :name A))
:content
(iota ?x (UKPrimeMinister ?x))
:language FIPA-SL
:reply-with query2)
(inform
:sender (agent-identifier :name A)
:receiver (set (agent-identifier :name B))
(= (iota ?x (UKPrimeMinister ?x))
"Tony Blair")
:in-reply-to query2)
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Interaction protocols
Specify agent communication patterns.
Example:
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FIPA interaction protocols
Request
Query
Request When
Contract Net
Iterated Contract Net
English Auction
Dutch Auction
Brokering
Subscribe
Propose
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Questions?
Comments.
Objections!
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Ontology
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Ontology
How do communicating agents understand each other?
Systems which communicate and work together must share an ontology.
A common vocabulary and agreed upon meanings to describe a subject domain.
An ontology is a specification of a conceptualization.
An ontology is a description of the concepts and relationships that can exist for an agent or a community of agents.
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Ontology
The shared ontology can be implicit or explicit.
Implicit ontology are typically represented only by procedures.
Explicit ontology are (ideally) given a declarative representation in a well defined knowledge representation language.
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Ontology
Example
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OWL (Web Ontology Language) by W3C
OWL is designed for use by applications that need to process the content of information instead of just presenting information to humans.
OWL is an extension to RDF and RDF Schema
Three sublanguages:
OWL Lite
classification hierarchy and simple constraints
OWL DL
maximum expressiveness, computational completeness, and decidability
OWL Full
maximum expressiveness and the syntactic freedom of RDF with no computational guarantees.
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OWL
OWL extends vocabulary description allowing to express claims such as...
Nothing can be both wine and beer.
grandParent and grandChild are inverses.
homepage is uniquely identifying property.
A W3CTeamPerson is a Person whose workplaceHomepage is
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OWL
OWL adds value to the Semantic Web
allowing machine-checkable ontology design
allowing inferring new knowledge
allowing automatic detection of contradictions
providing principles for complex data merging
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Environments of Agent
Another Viewpoint
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Environments of software agents - another viewpoint
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.
Most moderately complex environments (including, for example, the everyday physical world and the Internet) are inaccessible.
The more accessible an environment is, the simpler it is to build agents to operate in it.
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Environments of software agents
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.
The physical world can to all intents and purposes be regarded as non-deterministic.
Non-deterministic environments present greater problems for the agent designer.
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Environments of software agents
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.
Episodic environments are simpler from the agent developer’s perspective because the agent can decide what action to perform based only on the current episode — it need not reason about the interactions between this and future episodes.
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Environments of software agents
Static vs dynamic
A static environment is one that can be assumed to remain unchanged except by the performance of actions by the agent.
A dynamic environment is one that has other processes operating on it, and which hence changes in ways beyond the agent’s control.
The physical world is a highly dynamic environment.
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Environments of software agents
Discrete vs continuous
An environment is discrete if there are a fixed, finite number of actions and percepts in it.
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Questions?
Comments.
Objections!
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Readings:
Agent Technology Edited by Nicholas R. Jennings and Micheal J. Wooldridge Springer, 1998, ISBN
Mobile Agents and Security Edited by Giovanni Vigna Springer, ISBN
Mobile Agents William T. Cockayne, Micheal Zyda Manning, ISBN
Intelligent Software Agents Wlater Brenner, Rudiger Zarnekow, Hartmut Wittig Springer, 1998
Agent Technology - Foundations, Applications, and Markets Nicholas R. Jennings and Michael J. Wooldridge Springer, 1998
Multi-Agent Systems, An Introduction to Distributed Artificial Intelligence Jacques Ferber, Addison-Wesley, 1999
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