Transcript Autonomous Agents - New Mexico State University

Autonomous Agents
Overview
Topics
• Theories: logic based formalisms for the
explanation, analysis, or specification of
autonomous agents.
• Languages: agent-based programming
languages.
• Architectures: integration of different
components into a coherent control
framework for an individual agent.
Topics
• Multi-agent architectures: methodologies
and architectures for group of agents
(could be from different architectures)
• Agent modeling: modeling other agents’
behavior or mental state from the
perspective of an individual agent
• Agent capabilities
• Agent testbeds and evaluation
Agent Theories, Languages,
and Architectures
Wooldridge & Jennings
(ATAL 1994, LNAI 890)
What is an agent?
• Weak:
–
–
–
–
Autonomy
Social ability
Reactivity
Pro-activities
• Strong:
– Mental properties such as knowledge, belief,
intention, obligation
– Emotional
• Others attributes: mobility, veracity,
benevolence, rationality
Agent Theories
• How to conceptualize agents?
• What properties should agents have?
• How to formally represent and reason
about agent properties?
Agent Theories
• Definition: an agent theory is a
specification for an agent.
 Formalisms for representing and reasoning
about agent properties
• Starting point: agent = entity ‘which
appears to be the subject of beliefs,
desires, etc.’
Intentional system
• An intentional system whose behavior can
be predicted by the method of attributing
belief, desires, and rational acumen
• Proved that can be used to describe
almost everything
• Good as an abstract tool for describing,
explaining, and predicting the behavior of
complex systems
Intentional system - Examples
• One studies hard because one wants to get
good GPA.
• One takes the course ‘cs579-robotic’ because
one believes that it will be fun.
• One takes the course ‘cs579-robotic’ because
there is no 500-level course offered.
• One takes the course ‘cs579-robotic’ because
one believes that the course is easy 
Agent Attitudes
• Information attitudes: related to the information that an
agent has about the environment
– Belief
– Knowledge
• Pro-attitudes: guide the agent’s actions
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–
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–
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Desire
Intention
Obligation
Commitment
Choice
• An agent should be represented in terms of at least one
info-attitude and one pro-attitude. Why?
Representing intentional notions
Representing
Jan believes Cronos is the father of Zeus
naïve translation into FOL:
Believe(Jan, Father(Zeus,Cronos))
 Problems:
1. No nested predicate
2. Zeus = Jupiter
 Believe(Jan, Father(Jupiter,Cronos)) [Wrong]
Conclusion: FOL is not suitable since intention is
context dependent.
Possible World Semantics
• Hintikka: 1962 – Agent’s belief can be
characterized as a set of possible worlds.
• Example:
– A door opener robot: door is closed, lock needs to be
unlocked but the robot does not know if the lock is
unlocked or not – two possibilities:
• {closed, locked}
• {closed, unlocked}
– Card player (poker): ?
– UNIX Ping command: ?
Possible World Semantics
• Each world represents a state that the agent
believes it might be in given what it knows.
• Each world is called a epistemic alternative.
• The agent believes in something is true in all
possible worlds.
• Problem: logical omniscience – agent believes
all the logical consequences of its belief 
impossible to compute.
Alternatives to PWS
• Levesque – belief and awareness: explicit belief
(small) from implicit belief (large).
– No nested belief
– The notion of a situation is unclear
– Under certain situation: unrealistic prediction
• Konolige – the deduction model: modeling the
belief of a symbolic AI system (database of
beliefs and an inference system).
– Simple
Others
• Meta-language: one in which it is possible
to represent the properties of another
language
– Problem: inconsistency
• Pro-attitudes: goals and desires –
adapting possible world semantics to
model goals and desires
– Problem: side effects
Theory of agency
• Realistic agent:
– combination of different components
– dynamic aspect
• Moore – knowledge and action: study the
problem of knowledge precondition for actions
– I needs to know the telephone number of my friend
Enrico in order to call him.
– I can find the telephone number in the telephone
book.
– I needs to know that the course is easy before I sign
up for it 
Theory of agency
• Cohen and Levesque – belief and goal:
originally developed as a pre-requisite for
a theory of speech acts but proved very
useful in analysis of conflict and
cooperation in multi-agent diaglogue,
cooperative problem solving
Theory of agency
• Rao and Georgeff – belief, desire,
intention (BDI) architecture: logical
framework for agent theory based on BDI,
used a branching model of time
• Singh: logics for representing intention,
belief, knowledge, know-how,
communication in a branching-time
framework
Theory of agency
• Werner: general model of agency based
on work in economics, game theory,
situated automate, situated semantics,
philosophy.
• Wooldridge: modeling multi-agent system
Agent Architectures
• Construction of computer systems with
properties specified by an agent theory.
• Three well-know architectures:
– Deliberative
– Reactive
– Hybrid
Deliberative architecture
• View agent as a particular type of knowledge
based system – known as symbolic AI
• Contains an explicit represented, symbolic
model of the world
• Decision is made via logical reasoning (pattern
matching, symbolic manipulation)
• Properties:
– Attractive from the logical point of view
– High computational complexity (FOL: not decidable,
with modalities: highly undecidable)
Sense
• Assimilate
Sensing results
Reasoning
• Symbolic
representation
of the world
• Determine what
to do next
Act
• Execute the
action generated
by the reasoning
module
ENVIRONMENT
Deliberative architecture in picture
Deliberative architecture
• Examples:
– Planning agents: a planner is an essential component
of any artificial agent
• Main problem: intractability – addressed by techniques such
as hierarchical, non-linear planning.
– IRMA (Intelligent Resource-bounded machine
architecture): explicit representations of BDI &
planning library, a reasoner, opportunity analyser, a
filtering process, a deliberation process (mainly:
reduced the time to deliberate)
Deliberative architecture
• HOMER: a prototype of an agent with
linguistic capability, planning and acting
capability.
• GRATE*: layered architecture in which the
behavior of an agent is guided by the
mental attitudes of beliefs, desires,
intentions, and joint intention.
Reactive architecture
• Proposed to overcome the weakness of
symbolic AI
• Main features:
– does not include any kind of central symbolic
world model
– does not use complex reasoning
Sense
• Assimilate
Sensing results
Reasoning
• Determine what
to do next
Act
• Execute the
action generated
by the reasoning
module
ENVIRONMENT
Reactive architecture in picture
Reactive architecture
• Brook - behavior language: subsumption
architecture
– Hierarchy of task-accomplishing behaviors
– Each behavior competes with others
– Lower layer represents more primitive task
and has precedence over upper layers
– Very simple
– Demonstrate that it can do a lot
– Multiple subsumption agents
Reactive architecture
• Arge and Chapman – PENGI: most
everyday activity is ‘routine’
– Once learned, a task becomes routine and
can be executed with little or no modification
Routines can be compiled into a program and
then updated from time to time (e.g. after new
tasks are added)
Reactive architecture
• Rosenschein and Kaelbling - Situated
automata
– Agent is specified in declarative terms which
are then compiled into digital machine
– Correctness of the machine can be proved
– No symbol manipulation in situated automata,
thus efficient
• Maes – Agent network architecture: an
agent is a network of competency modules
Hybrid architecture
• Combine deliberative and reactive architecture –
exploit the best out of the two
• Georgeff and Lansky – Procedural Reasoning
System: BDI & plan library, explicit symbolic
representation of BDI
– Beliefs are facts – FOL
– Desires are represented by behavior
– Each plan in the plan library is associated with
invocation condition  reactive
– Intention – the set of currently active plans
Environment
Belief:
FOL
System
Interpreter
P1:
Invocation I1
Desire:
System beha.
Plan
Library
Intention:
Pn:
Invocation In
Pi:
Invocation Ii
Pj:
Invocation Ij
Active
PRS in picture
Hybrid architecture
• Ferguson – TOURINGMACHINES:
– Perception and action subsystem – interact directly
with the environment
– Control framework system: three control layers –
each is independent, activity producing, concurrently
executing process
• Reactive layer (response to events that happen too quickly
for other to response)
• Planning layer (select plan, actions to achieve goal)
• Modeling layer (symbolic representation, use to resolve goal
conflict)
Hybrid architecture
• Burmeister et al. – COSY: hybrid BDI with
features of PRS and IRMA, for a multiagent testbed called DASEDIS
• Mueller et at. – INTERRAP: layered
architecture, each layer is divided into
knowledge and control vertical part
Agent language
• A system that allows one to program hardware
and software computer systems in terms of
some of the concepts developed by agent
theorists.
• Shoham – agent-oriented programming:
– A logical system for defining the mental state of
agents
– An interpreted programming language for
programming agents
– An ‘agentification’ process, for compiling agent
program into low-level executable systems
 Agent0: first two features
Agent language
• Thomas – PLACA (Planning
communicating agent language)
• Fisher – Concurrent METATEM:
correctness of the agents with respect to
their specification
• IMAGINE project: ESPIRIT
• General Magic, Inc. – TELESCRIPT
• Connah and Wavish - ABLE