Table Of ContentAGENT AUTONOMY
MULTIAGENT SYSTEMS,
ARTIFICIAL SOCIETIES,
AND SIMULATED ORGANIZATIONS
International Book Series
Series Editor: Gerhard Weiss
Technische Universitat Miinchen
Editorial Board:
KathleenM. Carley,CarnegieMellon University,PA, USA
Yves Demazeau,CNRS LaboratoireLEIBNIZ,France
EdDurfee,UniversityofMichigan,USA
Les Gasser, UniversityofIllinois atUrbana-Champaign,Il.,USA
Nigel Gilbert,UniversityofSurrey,United Kingdom
MichaelHuhns,UniversityofSouth Carolina,SC, USA
NickJennings, UniversityofSouthampton,UK
VictorLesser,UniversityofMassachusetts,MA, USA
KatiaSycara,CarnegieMellon University,PA, USA
Gerhard Weiss,TechnicalUniversityofMunich,Germany (SeriesEditor)
MichaeJWooJdridge, UniversityofLiverpool,United Kingdom
Booksin the Series:
CONFLICTINGAGENTS: Conflict ManagementinMulti-AgentSystems,edited
byCatherineTessier,LaurentChaudronandHeinz-JurgenMUller,ISBN:0-7923
7210-7
SOCIALORDERIN MULTIAGENTSYSTEMS,edited byRosariaConte and
ChrysanthosDellarocas,ISBN:0-7923-7450-9
SOCIALLYINTELLIGENTAGENTS: CreatingRelationshipswithComputers
and Robots, editedbyKerstin Dautenhahn,AlanH.Bond, Lola Cafiameroand
Bruce Edmonds, ISBN: 1-4020-7057-8
CONCEPTUALMODELLINGOFMULTI-AGENTSYSTEMS: The
CoMoMASEngineeringEnvironment,byNorbert Glaser,ISBN: 1-4020-7061-6
GAMETHEORYAND DECISIONTHEORYIN AGENT-BASEDSYSTEMS,
edited bySimon Parsons,PiotrGmytrasiewicz,MichaelWooldridge,ISBN: 1-4020
7115-9
REPUTATIONIN ARTIFICIALSOCIETIES: SocialBeliefsforSocialOrder,
byRosariaConte, MarioPaolucci,ISBN: 1-4020-7186-8
AGENT AUTONOMY
Edited by
Henry Hexmoor
University ofA rkansas
Cristiano Castelfranchi
National Research Council, Italy
Rino Falcone
National Research Council, Italy
SPRINGER SCIENCE+BUSINESS MEDIA, LLC
Library of Congress Cataloging-in-PubUcation Data
ISBN 978-1-4613-4833-7 ISBN 978-1-4419-9198-0 (eBook)
DOI 10.1007/978-1-4419-9198-0
Agent Autonomy, edited by Henry Hexmoore, Cristiano Castelfranchi and Rino Falcone
A C.I.P. Catalogue record for this book is available
from the Library of Congress.
Copyright @2003 by Springer Science+Business Media New York
Originally published by Kluwer Academic Publishers in 2003
Softcover reprint ofthe hardcover Ist edition 2003
AII rights reserved. No part of this work may be reproduced, stored in a retrieval system,
or transmitted in any form or by any means, electronic, mechanical, photocopying,
microfilming, recording, or otherwise, without the written permission from the Publisher,
with the exception of any material supplied specifically for the purpose of being entered and
executed on a computer system, for exclusive use by the purchaser of the work.
Printed on acid-free paper.
Contents
1. AProspectusonAgentAutonomy 1
HENRYHEXMOOR,CRISTIANOCASTELFRANCHI, RINOFALCONE
2. Autonomy:VariableandGenerative 11
MICHAELLUCK, MARKD'INVERNO, STEVE MUNROE
3. RepresentingandAnalyzingAdaptiveDecision-MakingFrameworks 29
K. S. BARBER,I. M. GAMBA, C. E. MARTIN
4. QuantifyingRelativeAutonomyinMultiagentInteraction 55
SVIATOSLAVBRAYNOV,HENRY HEXMOOR
5. ObligationsandCooperation:TwoSidesofSocialRationality 75
GUIDO BOELLA
6. FromAutomaticitytoAutonomy:TheFrontierofArtificialAgents 103
CRISTIANOCASTELFRANCHI, RINOFALCONE
7. AdjustingtheAutonomyinMixed-initiativeSystemsbyReasoning 137
aboutInteraction
ROBIN COHEN, MICHAELFLEMING
8. InteractingwithIDA 159
STANFRANKLIN,LEEMCCAULEY
vi Contents
9. Policy-basedAgent Directability 187
KARENL. MYERS, DAVID N.MORLEY
10. AdjustableAutonomy for the Real World 211
PAULSCERRI,DAVIDPYNADATH,MILINDTAMBE
11. AdjustableAutonomy and Human-AgentTeamworkinPractice: 243
An InterimReporton Space Applications
JEFFREYM. BRADSHAW, MAARTEN SIERHUIS, ALESSANDROACQUISTI,
PAUL FELTOVICH, ROBERT HOFFMAN, RENIA JEFFERS, DEBBIE
PRESCOTT,NIRANJANSURI, ANDREZEJUSZOK, RON VANHoof
Index 281
Chapter 1
A Prospectus on Agent Autonomy
HENRYHEXMOOR*,CRISTIANOCASTELFRANCHI**andRINO
FALCONE**
*ComputerScience&ComputerEngineeringDepartment, EngineeringHall,Room313,
Fayetteville,AR72701
**InstituteofCognitiveSciencesandTechnologies-NationalResearchCouncil,Rome,Italy
Keywords: interaction,dependence,mixedinitiative,adjustableautonomy
Abstract: Thispapersummarizesthestateofart inagentautonomy.It dispelsmythsand
builds a foundationfor studyof autonomy.Wepoint to arenewed interest in
good old-fashioned AI that has emerged from consideration of agents and
autonomy.Thispaperalsoservesasareader'sguidetothepaperinthisbook.
Weendwithsoberingthoughtsaboutthefutureofthehumanrelationshipwith
machines.
1. AGROWINGDOMAIN
There have been many recent meetings that have explored the issues of
autonomy in agents-in its meaning, in its operationalization, and its
consequences.In the early 1990s a series of workshops focused on architectures
and this gradually shifted to address autonomy more directly (AAAI 1991-1995,
2000, IJCAI 1999,AA 1999).Anotherstrong driving factor wasthe proliferation
ofspace missions that moreexplicitly focused on theproblem ofinteraction with
an autonomous agent and the "adjustability" of this autonomy (Dorais, et aI.,
1998;Bradshaw,et aI.,2001).This clearly is aconsistent trend in AI and human
machine interaction.
H. Hexmoor et al. (eds.), Agent Autonomy
© Springer Science+Business Media New York 2003
2 Chapter1
This volume contains several papers on the state of art in the field and
illustrates maturing trends ranging from analytical and formal approaches to
concrete attempts and application problems. This book also challenges and
provokes ustoreturn toissues ofgood old-fashionedAI,as weexplain next.
2. WELCOME BACK GOOD OLDAI: TOWARDS
(INTELLIGENT)AUTONOMOUSAGENTS?
Some sort of "agentification" is sweeping the entire field of AI. In fact, the
agent-basedapproach isbecomingsynonymouswith AI.Itisnot onlya matter of
cultural fashion or of emergence of topics as it was the case with introduction of
expert systems. A prime reason for this transformation is that the agent-based
approach offers solutions for advanced computational problems that are robust,
distributed, flexible, and scalable. But there are also cultural and theoretical
underpinnings. Agents haveresuscitatedallthe original challenges, provocations,
and ambitions in the good old-fashioned AI of Turning. Crucial issues like
emotions, mind-body relation, experience and coupling with an external world,
learning, etc., that were central in the discussion between Turing and
philosophers about the possibility of realizing artificial intelligence, are now
resuscitated. In the last 15 years, each of these areas has become an important
branch ofAI. Among those issues are autonomy and initiative. Initiative is pro
actively reasoning about, the course of problem solving. This might include
dialogues ortasks. Autonomy isreasoning about delegation anddependence, and
is closely affiliated with initiative. This has been at the core of contention
between Turingand Popper. Popper's final objection and obstruction to AI isthe
following:
Oneday,Turingwastalkingontheradioandheissuedthechallenge,
claiming something like"Describe me what- in yourview- a computer
cannotdo, ... andI willbuildoneonpurpose".Thefamous philosopher
Karl Popperwasratherscandalised andwrote hima letterclaiming that
thereis something in particular thatcomputers do not haveand cannot
have: initiative!Wecannot'describe'initiative, although itissomething
thatanylittlechildandanimalhas.
In fact, we are building agents and robotic systems that exhibit nontrivial
initiative and therefore agents can be considered to be autonomous. Sharing
initiative with machines is also the reason we are concerned with "mixed
initiative" in human-computerinteraction, or about "adjustable autonomy".Here
we will not obsess with such foundational issues.Instead, we will keep in mind
the range and significance of our objectives-that is, to build methods that
harness thepower ofmachines with initiative.
A Prospectus onAgentAutonomy 3
3. WHATISAUTONOMY?
Fromsciencefictiontoaffectionate references,humanfantasiesaboutnaming
machines and interacting with them socially serve a useful function for human
psychology but do not advance our understanding of autonomy. It is possible to
build useful social agents as in (Breazeal 1999) that affect human emotion.
However, mere anthropomorphism or human-like emotion-based behavior does
not affect autonomy. Autonomy consideration makes sense when the machine
cannontriviallyandpurposivelychange itsinteraction.
Autonomy is a characterizing notion of agents, and intuitively it is rather
unambiguous. We recognize the quality of autonomy when we perceive or
experience it. Yet, it is difficult to limit it in a definition. The desire to build
agents that exhibit a satisfactory quality of autonomy has included agents that
have a long life, are highly independent, can harmonize their goals and actions
with humans and other agents, and are generally socially adept. We will try to
approximate our intuition, to dispel false attributions, and to point the way to
scholarlythinkingaboutautonomy.
Let's consider two types of interaction for study of autonomy. The first is
interaction between human and machine. In this type of interaction, autonomy
concerns are predominantly for the agent to acquire and to adapt to human
preferences and guidance. The reference point in this style of interaction is
alwaysthehuman,and thisgives us arelative sense ofautonomy.Inthis relative
sense, we are concerned with relative deviations in the agent's attitudes and
functioning with respect to the human user. The word "autonomous" connotes
this relative sense of the agent's autonomy from the human. A device is
autonomous when the device faithfully carries the human's preferences and
performs actions accordingly. For instance, consider an agent in service of a
human. The agent is said to be fully autonomous when it has access to the
complete set of choices and preferences of its user. Here the user is a
distinguished entity that might judge or change an agent's autonomy. The idea
that an agent's autonomy can be adjusted to match the pace of the human is
termedadjustable autonomy (MuslinerandPell, 1999).
Relative autonomy also makes sense in another type of interaction, which is
among a group of agents. In these interactions an agent's autonomy can be
considered relativeto another agent or an environmental factor. There is no user
butanyotheragentmaybe thereference point.
Autonomy is a social notion, and in fact research has been linked to many
social theories. Delegation theory is one such social theory. In many cases the
user (or the delegating agent) needs local and decentralized knowledge and
decision from thedelegated agent.This agent- delegated to take care of agiven
task-- has to choose from among different possible recipes (plans), or to adapt
abstract or previous plans to suit new situations; it has to find additional (local
4 Chapter1
and updated) information; it has to solve a problem and not just to execute a
function, an action, or implement a recipe; sometimes it has to exploit its
"expertise". Inall thesecases thisagent takescareofthe interests orgoals of the
former "remotely", i.e., far from it and without its monitoring and intervention
(control), and autonomously. This requires what is called an "open delegation":
basically the delegation "to bring it about that..." (Castelfranchi and Falcone,
1998).Theagentissupposedtouseitsknowledge,itsintelligence,anditsability,
andtoexertadegree ofdiscretion.
Control and autonomy are related. When agents consent to a balance of
controlbetween them, theirbalance ofautonomyissomewhat complementary to
their control. In other words, control affects autonomy. The reverse does not
hold. Between an agent that has larger autonomy and another with lower
autonomy, there may not be a control relationship. Over a common set of
choices, autonomy ofthe agent who hasagreedtobecontrolled over thechoices
islowerthantheagent whoiscontrolling.Exertingcontrolisthedegreetowhich
power is asserted. Experiencing control is the amount of power an agent feels
powerimposed. Accomplished control istheamountto which anagent concedes
topowerasserted.Naturally, thedegree of autonomyisaffectedbycontroltothe
extenttheagentallowscontrol.To theextenttowhichthismeasureisbinary, the
relationshipbetweencontrol andpowerisclosetobeingcomplementary.
So far we have described an operational sense of autonomy. Beyond this
functional perspective, one can consider an agent's internal manipulation of its
own capabilities, its own liberties and what it allows itself to experience about
the outside world as a whole. Margaret Boden defines behavioral autonomy as
the agent's capacity to be original and not guided by outside sources (Boden
1996). In this view, an agent formulates a liberty over how it functions in the
world. It is not nuanced based on one thing or another. There have been few
elaborationsofthisinthe literature.
It is important to point out certain works that remain outside the pursuit of
autonomy. When machines perform tasks that require cognitive reasoning, we
judge the machine's ability to explicitly reason when attributing autonomy.
However complex, machines that do not have an explicit reasoning module
shouldnotbeconsidered inthe same light.An automatedchess-playing machine
may nothave much autonomy. Autonomy is considered if thismachine is aware
ofitsinteractions andusesthatinitsactionselection.
Mere adaptation or compliance is not enough either. A robot might suitably
conform to a variety of circumstances and interactions. However, unless it is
purposeful,itispassive.We wouldliketo alsoexcludemuchofsystemsthatuse
machine learning. Human subjective judgment of a machine's appropriate and
timely behavior is not a litmus test of its autonomy. We can design a machine
thatis, or learns to become, socially adept.In general, thisis independent of the
machine's autonomy. We would consider a change in its autonomy if the
