Download Joint issue with NOVÁTICA Artificial Intelligence

The European Online Magazine for the IT Professional
http://www.upgrade-cepis.org
Vol. III, No. 5, October 2002
UPGRADE is the European Online Magazine
for the Information Technology Professionals,
published bimonthly at
<http://www.upgrade-cepis.org/>.
Publisher
UPGRADE is published on behalf of CEPIS (Council of
European Professional Informatics Societies,
<http://www.cepis.org/>) by NOVÁTICA
<http://www.ati.es/novatica/>, journal of the Spanish CEPIS
society ATI (Asociación de Técnicos de Informática
<http://www.ati.es/>).
UPGRADE is also published in Spanish (full issue printed, some
articles online) by NOVÁTICA, and in Italian (abstracts and some
articles online) by the Italian CEPIS society ALSI
<http://www.alsi.it> and the Italian IT portal Tecnoteca
<http://www.tecnoteca.it/>.
UPGRADE was created in October 2000 by CEPIS and was first
published by NOVÁTICA and INFORMATIK/INFORMATIQUE,
bimonthly journal of SVI/FSI (Swiss Federation of Professional
Informatics Societies, <http://www.svifsi.ch/>).
Chief Editors
François Louis Nicolet, Zürich <[email protected]>
Rafael Fernández Calvo, Madrid <[email protected]>
Artificial Intelligence: Technology with a Future
Guest Editors: Federico Barber, Vicente J. Botti, and Jana Koehler
Joint issue with NOVÁTICA
2 AI: Past, Present and Future
– Federico Barber, Vicente J. Botti, and Jana Koehler
The guest editors present the issue and include a list of useful references for those interested in
knowing more about Artificial Intelligence.
6 Spoken Communication with Computers – Francisco Casacuberta-Nolla
This article deals with the development of systems which enable spoken interaction with computers,
of widespread use in speech recognition systems, translation systems, etc.
10 Progress in AI Planning Research and Applications – Derek Long and Maria Fox
In this paper the autors sketch the foundations of planning as a sub-field of Artificial Intelligence
and the history of its development over the past three decades, and discuss some of the recent
achievements within the field.
25 Trends in Automatic Learning – Ramón López de Mántaras
Editorial Board
Prof. Wolffried Stucky, CEPIS President
Fernando Piera Gómez and
Rafael Fernández Calvo, ATI (Spain)
François Louis Nicolet, SI (Switzerland)
Roberto Carniel, ALSI – Tecnoteca (Italy)
32 Knowledge-Based Systems – José Mira-Mira and Ana E. Delgado-García
English Editors: Mike Andersson, Richard Butchart, David
Cash, Arthur Cook, Tracey Darch, Laura Davies, Nick Dunn,
Rodney Fennemore, Hilary Green, Roger Harris, Michael Hird,
Jim Holder, Alasdair MacLeod, Pat Moody, Adam David Moss,
Phil Parkin, Brian Robson.
39 Cooperating Physical Robots and Robotic Football
– Bernhard Nebel and Markus Jäger
Cover page designed by Antonio Crespo Foix, © ATI 2002
Layout: Pascale Schürmann
This article looks at intelligent IT systems’ learning capacity, one of the fundamental characteristics
of intelligence, and the techniques they employ to develop it presently.
In this article Knowledge Engineering is presented with special emphasis on methodological
aspects (Knowledge Based Systems, Expert Systems), with the aim of approaching the rigour of
other engineering disciplines.
In this article an analysis is made of the techniques and applications related to physical robots in
tasks carried out in real environments, where the ability of the robots to cooperate correctly is
especially important.
E-mail addresses for editorial correspondence:
<[email protected]> and <[email protected]>
46 Autonomous Agents and Multi-Agent Systems – Carles Sierra
E-mail address for advertising correspondence:
<[email protected]>
53 Artificial Intelligence and Education: an Overview
– Maite Urretavizcaya-Loinaz and Isabel Fernández de Castro
Copyright
© Novática. All rights reserved. Abstracting is permitted with
credit to the source. For copying, reprint, or republication
permission, write to the editors.
This article presents the current state of multi-agent systems and their main applications.
This paper offers an overview of the different contributions AI is making to the world of educational
IT, and a review of intelligent educational systems.
The opinions expressed by the authors are their exclusive
responsibility.
ISSN 1684-5285
Coming issue:
“Security in
E-Commerce/Business”
1
Artificial Intelligence: Technology with a Future
Presentation
AI: Past, Present and Future
Federico Barber, Vicente J. Botti, and Jana Koehler
Artificial Intelligence (AI), defined as “the science of making
machines do things that would require intelligence if done by
men” (Minsky), took on a viable scientific meaning as a
modern Computer Science (CS) discipline during the second
half of the 20th century. It was the direct result of the convergence of various intellectual currents (Theory of Computation,
Cybernetics, Information Theory, Symbolic Processing,
Psychology, …) which had developed from the formal bases of
Logic and Discrete Mathematics, and had been given impetus
by the development of digital computers. AI represents a serious effort to understand the complexity of human experience in
information processing terms. It deals not only with how to
represent and use complex and incomplete information logically, but also with questions of how to see (vision), move (robotics), communicate (natural language, speech), learn, etc.
Human intelligent behaviour of the sort that AI tries to emulate comprises several different aspects. One deals mainly with
cognitive reasoning processes and is clearly related to logic.
Another is more that of a perceptive nature (vision, speech,
etc.) and, although it shares some problems and methods with
the previous aspect, it tends to be more rigorous in terms of
formal expression and its specific problems, techniques and
methods constitute the discipline known as Pattern Recognition. Finally we can talk about symbolic AI, concerned with the
processing of symbols of knowledge, and connectionist AI, in
Federico Barber, Telecommunications Engineer and Doctor of
Computer Science, is currently a Full Professor at the Universidad
Politécnica de Valencia (Spain), where has been the Dean of the
Faculty of Computer Science. He has been the editor of “Inteligencia Artificial, Revista Iberoamericana de IA”‚ (an Ibero-American
AI journal), and he is currently President of the Spanish Association
for Artificial Intelligence(AEPIA). His areas of study are centred
mainly on the problems of constraint satisfaction (scheduling,
optimization, temporal planning with resources, temporal reasoning, etc.) in which he has developed his own models and applications, in addition to the field of knowledge engineering. He is joint
leader of an extensive research group and has published a great
many scientific articles. He has also participated in or led national
and international research projects (CICYT, MC&T, ESPRIT, etc.),
and technology transfer agreements, as well as sitting on various
scientific committees in his field. He is a senior member of ATI and
co-editor of Novática’s AI section. <[email protected]>
Vicente J. Botti, Electrical Engineer and Doctor in Computer
Science, is currently a Full Professor at the Universidad Politécnica
de Valencia (Spain), where he has also been the Head of the Dept.
of Informatics Systems and Computation. His fields of study are
focused mainly on multi-agent systems, and more specifically, real
© Novática
which the process of intelligence is simulated by means of
basic, usually quantitive, elements of processing.
If we look at just the common core of AI, there is a wide
range of trends which consider aspects of both human thought
and human behaviour. Each of these trends can in turn receive
empirical approaches which use hypothesis and subsequent
confirmation by experiment, or rationalist approaches which
require a combination of logic-mathematical and engineering
processes (see Table 1).
The approaches contained in Table 1 define AI according to
each of these different aspects. The definitions in the top part
are focused on processes connected with reasoning or thought
and the ones in the lower part are focused on processes related
to behaviour. The definitions in the left column measure the
success of AI from the human perspective (which requires an
empirical approach) and those in the right column do the same
from a rational perspective, a concept of intelligence which
could be called rationality.
In recent years, research into AI has undergone a marked
change with regard to both content and the methodology being
used. It is becoming ever more common to build AI systems
based on existing theories rather than putting forward new
theories; taking as a starting point rigorous theorems or solid
experimental evidence rather than intuition, and demonstrating
the use of AI applications in the real world rather than creating
time multi-agent systems, real time systems, mobile robotics (in
which he has developed his own models, architectures and applications) in addition to the field of knowledge engineering. He is joint
leader of an extensive research group whose general line of
research is Artificial Intelligence and has published about 100
scientific articles. He has been and is a principal researcher on
nationally and internationally funded projects (CICYT, MC&T,
ESPRIT, etc.), and on technology transfer agreements, as well as
sitting on various scientific committees in his areas of interest. He
is a senior member of ATI and co-editor of Novática’s AI section.
<[email protected]>
Jana Koehler is a research staff member and project leader at the
IBM Research Lab in Zurich that she joined in Spring 2001. She got
her Phd in 1994 from the University of Saarbruecken, where she
had worked at the German Research Centre for AI from 1990 to
1995 in an AI planning project. From 1996 to 1999 she was an
assistant professor at the University of Freiburg where she started
working as a consultant for the technology management of Schindler Elevators in 1998. From 1999 to 2001 she worked as a project
leader for Schindler Elevators. At IBM, she works on new middleware technology for the integration and automation of business
processes based on webservices. <[email protected]>
UPGRADE Vol. III, No. 5, October 2002
2
Artificial Intelligence: Technology with a Future
Human
Thought
Empirical Approach
Rational Approach
Systems which think like humans.
Systems which think rationally.
Cognitive Science.
Logic-mathematical processes.
"The exciting new effort to make computers think ... machines "The study of mental faculties through the use of computational
with minds, in the full and literal sense".
models ".
"The automation of activities that we associate with human
thinking, activities such as decision-making, problem solving,
learning...".
Systems which act like humans.
Human
Behaviour
"The study of the computations that make it possible to
perceive, reason, and act".
Systems which act rationally.
Cognitive task simulation.
Implementation of Inferential Processes.
"The art of creating machines that perform functions that
require intelligence when performed by people".
"A field of study that seeks to explain and emulate intelligent
behaviour in terms of computational processes ".
"The study of how to make computers do things at which, at the "The branch of computer science that is concerned with the
moment, people are better".
automation of intelligent behaviour ".
Table 1: Various approaches to AI from different perspectives
Source: Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, 1995
‘toy’ examples. In areas such as games, logical inference and
theorem proving, and medical diagnosis, systems based on
rigorous theoretical principles are emerging which can perform
as well or better than human experts. In other areas – such as
learning – visual perception, robotics and natural language
understanding are making rapid steps forward thanks to the
application of better analytical methods and a better understanding of the problems involved.
A good example of the above is the field of natural language
understanding. In the 70s a great many architectures and ad hoc
approaches were tested on some specially chosen examples.
More recently these have given way to approaches based on
hidden Markov models, founded on rigorous mathematical
theory in which models are generated by means of a learning
process based on a large body of real language data. The use of
these models enables us to obtain ever better classifications,
and language technology, together with its associated field of
handwriting recognition, is currently moving towards industrial and consumer applications.
During the 90s, Fuzzy Logic was consolidated in several AI
contexts, and the connectionist paradigm continued to gain
favour, as did genetic algorithms, leading to the development of
hybrid systems in a quest for adaptability. New knowledge
acquisition methodologies were developed, such as KADS (by
Stewart Tansley). In learning significant advances were made
and new methods were put forward. With regard to cognitive
architectures we saw the revolution that the introduction of reactivity sparked in the development of autonomous agents.
Finally we witnessed a change of paradigm in artificial vision,
from the classic passive approach to the active approach (Alan
Yuille) whereby the perceptual task is connected with the
performance of actions (perception-action). This had important
implications for the development of robotic systems with
enhanced performance.
The work done by Tate and Chapman has given rise to an
elegant synthesis of planning programmes brought together in
a unified framework. Planning systems are currently used for
3
UPGRADE Vol. III, No. 5, October 2002
programming the work in factories and for space shots. Meanwhile, intelligent scheduling systems based either on the
Constraint Satisfaction Problems (CSP) paradigm extended by
the inclusion of temporal reasoning techniques, or knowledge
based systems, provide an alternative answer to classic unsolvable problems.
Pearl’s Probabilistic Reasoning in Intelligent Systems (1988)
marked the arrival of the use of probability and decision theory
in AI. The development of the belief network formalism
responded to the need to be able to reason efficiently when
faced with a combination of uncertain knowledge. This
approach far outperforms the probabilistic reasoning systems
of the 60s and 70s, and is currently at the heart of research into
AI which is being currently being carried out on uncertain
reasoning and expert systems (ES). The work of Pearl, Horvitz
and Heckerman served to promote the idea of ES rules, that is,
that they should act rationally in accordance with decision
theory, without trying to imitate human experts. Following this
line of thought, fuzzy logic, based on possibility theory,
emerged in response to the difficulty of providing problems
with precise data input. Possibility theory was introduced by
Zadeh in 1965 to handle uncertainty in fuzzy systems, and has
much in common with probability. Although mathematicians at
first considered it to be a flawed theory, possibility theory actually tackles a different problem. Fuzzy logic has been widely
used by the Japanese in the design and construction of household appliances.
Similar trends have been seen in robotics, computerized
vision, automatic learning (including neural networks) and
knowledge representation. A better understanding of the problems and their complexity, together with greater computing
capacity, have enabled sound reasoning methods to be created.
Possibly encouraged by the progress made in the solution of
subproblems in AI, researchers have gone back to work on the
problem of the “complete agent”, adopting this new, more
formalist, trend. Research by Newell, Laird and Rosenbloom
(SOAR) is the best known example of a general architecture for
© Novática
Artificial Intelligence: Technology with a Future
an AI system. One of the fundamental aspects of a general
architecture is its capacity to incorporate many different kinds
of decision making, from knowledge based deliberation to
reflex action responses. The new agent architectures aim to
strike a balance between these two factors, reflex responses, for
situations in the which speed is of the essence, and knowledge
based deliberations, where the agent has time to take more
information into consideration, for forward planning, for
handling situations in which there is no immediate response
available and to propose better responses tailored specifically
to the situation in hand. Architectures such as SOAR have
precisely this structure. By means of compilation processes
like explanation based learning, they convert declarative information at a deliberative decision making level into more efficient representations until the decision eventually becomes a
reflex action.
Research into real time AI looks into all the abovementioned
aspects. Agents in real environments need to have the means of
controlling their own deliberations and also be capable of using
the time allowed for reasoning to perform the calculations
which will provide the best results. As AI systems are applied
to ever more complex domains, so all the problems will
become real time problems, since the agent will never have
enough time to find an exact solution to a problem.
There is obviously a great need for methods which work well
in more general decision taking situations. In recent years two
promising techniques have appeared, anytime algorithms and
decision theory techniques. The last element of an agent’s
architecture is its learning mechanism. Inductive learning,
reinforcement learning and compilation learning mechanisms
can be used for all agent’s learning activities. These mechanisms will doubtless depend on the type of representation
chosen. Representations based on logic, and neural and probabilistic networks, are well known and much studied formalisms
for which there are a great variety of learning methods. As new
representations are created, such as first order probabilistic
logics, it will be necessary to create new learning algorithms
for them.
Agent/multi-agent system (MAS) technology is making
important contributions to problem solving in various domains
(e-commerce, e-auctions, medicine, stock market, manufacturing systems, telephony systems, etc.), where traditional approaches do not provide satisfactory solutions. The study of
Multi-Agent Systems began nearly 20 years ago, within the
area of Distributed Artificial Intelligence (DAI) which is a
subfield of artificial intelligence research. DAI is the study of
intelligent group behaviour stemming from the cooperation of
what are known as agents. It studies how a group of modules
cooperate to divide up and share the knowledge of a problem,
and how it reaches a solution. DAI focuses on global behaviour,
with a predetermined agent behaviour. It studies the techniques
and knowledge required for the coordination and distribution
of knowledge and actions in a multi-agent environment.
When we look at how AI has evolved in the last fifty years we
can see a transition from the initial embryonic theories and
systems to the adaptable, robust and user-friendly environments of today; environments based on a wide range of logical
© Novática
theories, cognitive models and engineering based approaches.
Technological development and progress in related fields (Neurophysiology, Psychology, Biology) will have a great deal to
say in the future. An analysis of current AI systems and the way
they can be extended will enable us to pose a great many questions, the answers to which will lead us towards general
purpose intelligent systems.
In this monograph by Upgrade we have introduced a few of
the areas and techniques involved in AI which, by their very
scope, are impossible to deal with comprehensively. We shall,
pay special attention to the discipline’s applicability and use as
an alternative solution where other techniques or methodologies have failed or do not provide satisfactory solutions, or
where these alternative techniques may provide better
solutions.
The articles included are the work of very important
researchers/developers and cover each of the areas dealt from a
multiple viewpoint – generalist, scientific and applied – with a
special emphasis on future development. These contributions
should give the reader an idea of the historical perspective, the
current state and the future possibilities of AI. We hope that
they will enable the reader to have a clearer understanding of
these areas and a greater awareness of the current realities and
the challenges they pose. The articles included are:
“Spoken Communication with Computers”, by Francisco
Casacuberta-Nolla, dealing with the development of systems
which enable spoken interaction with computers, of widespread use in speech recognition systems, translation systems,
etc.
“Progress in Planning Research and Applications”, by
Derek Long and Maria Fox, in which they take a look at the applications and current challenges posed by intelligent planning
techniques, used in task planning, robots, resource scheduling,
etc.
“Trends in Automatic Learning”, by Ramon López de
Mántaras, in which he looks at intelligent IT systems’ learning
capacity, one of the fundamental characteristics of intelligence,
and the techniques they employ to develop it.
“Knowledge-Based Systems”, by José Mira-Mira and Ana E.
Delgado-García. In this article Knowledge Engineering is
presented with special emphasis on methodological aspects
(Knowledge Based Systems, Expert Systems), with the aim of
approaching the rigour of other engineering disciplines.
“Cooperating Physical Robots and Robotic Football”, by
Bernhard Nebel and Markus Jäger. In this article an analysis is
made of the techniques and applications related to physical
robots in tasks carried out in real environments, where the ability of the robots to cooperate correctly is especially important.
“Autonomous Agents and Multi-Agent Systems”, by Carles
Sierra. This article presents the current state of multi-agent
systems and their main applications.
“Artificial Intelligence and Education: an Overview”, by
Maite Urretavizcaya-Loinaz and Isabel Fernández de Castro.
An overview of the different contributions AI is making to the
world of educational IT, and a review of intelligent educational
systems.
UPGRADE Vol. III, No. 5, October 2002
4
Artificial Intelligence: Technology with a Future
To close, we would like to thank all the participants in this
monograph for their interest and efforts, and to thank the
editors of Upgrade too for their support, suggestions and infinite patience in bringing this work to fruition.
society ATI (Asociación de Técnicos de Informática) at <http://www.
ati.es/novatica/>, and in Italian (online edition only, containing
abstracts and some articles) by the Italian CEPIS society ALSI and the
Italian IT portal Tecnoteca at <http://www.tecnoteca.it>.
Translated by Steve Turpin
Note from the Editors:
This monograph will be also published in Spanish (full issue printed, some articles online) by Novática, journal of the Spanish CEPIS
Useful AI References
Compiled by Federico Barber and Vicente J. Botti
Artificial Intelligence is an exceptionally lively field. In addition to its real practical applications, there is a great body of
research and development, a symptom of the great challenges
still to be met. Below we list just some of the references of
associations, conferences, prestigious publications, interesting
links, etc. which reflect the activity and development in this
subject. Without claiming to offer an exhaustive list we provide
a sample of some of the most important items, to which should
be added the references which appear in the articles comprising
this issue.
Principal Associations
IJCAI (International Joint Committee on AI)
ECCAI (European Coordination Committee for AI):
<http://www.eccai.org/>
AAAI (American Association for AI) <http://www.aaai.org/>
IBERAMIA (Association of Ibero-American Associations):
<http://www.iberamia.org/>.
AEPIA (Spanish Artificial Intelligence Association):
<http://www.aepia.org>
Some e-publications
JAIR. Journal of A.I. Research: <http://www.jair.org>
ETAIJ. Electronic Trans. on AI – ECAI: <http://www.etaij.org/>
Cognitive Systems Research:
<http://www.elsevier.com/locate/cogsys>
Some specific publications
AI Magazine (AAAI)
Artificial Intelligence
Artificial Intelligence Review
Cognitive Brain Research
Cognitive Science, etc.
Computer Speech and Language
Data & Knowledge Engineering
Data Mining and Knowledge Discovery
Electronic Trans. on AI (AI Communications).
Engineering Applications of Artificial Intelligence
Fuzzy Sets and Systems
IEEE Trans. on Pattern Analysis and Machine Intelligence
IEEE Transactions on Fuzzy Systems
IEEE Transactions on knowledge and data engineering.
IEEE Transactions on Man and Cybernetics
IEEE Transactions on Neural Networks
Int. J. of Uncertainty Fuzziness and Knowledge-Based Systems
Int. journal of Approximate Reasoning
Int. journal of Computer Vision
Intelligent Systems and their applications
International Journal of Pattern Recognition and A.I.
5
UPGRADE Vol. III, No. 5, October 2002
Knowledge Eng. Review.
Machine learning
Neural computation
Neural Networks
Pattern recognition.
Conferences and congresses
IJCAI: International Joint Conference on AI
ECAI: European Conference on AI
IEEE Conference on Artificial Intelligence
CAEPIA: Spanish Conference on Artificial Intelligence.
Distribution lists
INT-ARTIF: AI Distribution List (RedIRIS):
<http://www.rediris.es/list/info/int-artif.html> (in Spanish).
Newsgroups
comp.ai
comp.ai.edu
comp.ai.digest
comp.ai.doc-analysis
comp.ai.fuzzy
comp.ai.games
comp.ai.genetic
comp.ai.nat-lang
comp.ai.nlang-know-rep
comp.ai.neural-nets
comp.ai.phiñlosophy
comp.ai.shells
comp.ai.vision
Other interesting links
Spanish Artificial Intelligence Research Institute (IIIA), Spanish
Scientific Research Council (CSIC): <http://www.iiia.csic.es/>
News service of the AAAI:
<http://www.aaai.org/Pathfinder/html/current.html>
AI Topics: <http://www.aaai.org/Pathfinder/pathfinder.html>
TECNOCIENCIA. AI directory:
<http://www.portaltecnociencia.es/index/DirectorioSEC.jsp?ISI
=0616>
Artificial Intelligence Laboratory (MIT):. <http://www.ai.mit.edu/>
CMU Artificial Intelligence Repository:
<http://www.cs.cmu.edu/Web/Groups/AI/html/air.html>
Artificial Intelligence Resources in the Institute For Information
Technology: <http://ai.iit.nrc.ca/ai_point.html>
The Collection of Computer Science Bibliographies . Bibliographies
on Artificial Intelligence:
<http://liinwww.ira.uka.de/bibliography/Ai/index.html>
Computer Science Bibliography. Artificial Intelligence:
<http://www.informatik.uni-trier.de/~ley/db/ai.html>
© Novática