Download programme summary - Department of Informatics

15th European Agent Systems Summer
School
(tutorial abstracts∗)
June 5, 2013
Contents
1 Theme: Agents, from research to applications
1.1 Introduction to agent systems . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Autonomous intelligent agents in action: Agents on earth and in space . .
1.3 Agent and multi-agent research methodologies . . . . . . . . . . . . . . . .
2
2
2
2
2 Theme: Agent systems development
2.1 Agent-oriented programming . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Agent-based modelling and computational economics . . . . . . . . . . . .
2.3 Normative multi-agent systems . . . . . . . . . . . . . . . . . . . . . . . .
4
4
4
5
3 Theme: Agreement technologies
3.1 Voting theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Dialogue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Negotiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
6
6
6
4 Theme: Game theory
4.1 Game theory for computer science . . . . . . . . . . . . . . . . . . . . . .
4.2 Reasoning about cooperation . . . . . . . . . . . . . . . . . . . . . . . . .
7
7
7
5 Theme: Planning for agents
5.1 Introduction to AI planning . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Decision making under uncertainty . . . . . . . . . . . . . . . . . . . . . .
8
8
8
6 Theme: Verification of multi-agent systems
6.1 Model checking multi-agent systems . . . . . . . . . . . . . . . . . . . . .
6.2 Verifying agent-based autonomous systems . . . . . . . . . . . . . . . . . .
9
9
9
7 Tutors: biographical sketches
∗
10
Missing abstracts and biographical sketches will be updated later on.
1
1 Theme: Agents, from research to applications
1 Theme: Agents, from research to applications
1.1 Introduction to agent systems
Tutor: Michael Luck
This course will provide an introduction to the field of agents and multi-agent systems,
spanning both the micro and macro level, including: basic concepts; agent architecture;
interaction and multi-agent systems; applications; and future challenges.
1.2 Autonomous intelligent agents in action: Agents on earth and in
space
Tutor: Sara Bernardini
This tutorial focuses on autonomous intelligent agents and their applications in the
real world. Agents are autonomous when they are able to decide independently, i.e.
without human intervention, how to act best in order to achieve a set of high-level
goals that have been delegated to them. In order to act autonomously, depending on the
context of use, an agent needs to display a subset of a number of sophisticated properties:
deliberation capabilities, reactiveness, pro-activeness, social and emotional abilities. Over
the last twenty years, there has been continuous progress in achieving these high-level
behaviours and in integrating them in fully-fledged agent architectures. Autonomous
intelligent agents have proven successful in a number of different fields ranging from
manufacturing to space exploration to assistive technology. After an introduction on
autonomy and autonomous architectures from a theoretical point of view, this tutorial
shows the potential of the autonomous agent paradigm by presenting two successful realworld applications of autonomy in two different and unrelated fields: agent technology for
supporting learning in ill--defined domains such as social communication and emotional
regulation and agent technology for aeronautics and space missions. For each class of
applications, the tutorial describes the theoretical design principles underpinning systems
belonging to that class, the practical implementation of such systems and their evaluation
in the relevant contexts of use. Finally, the tutorial presents two case studies for each
class of applications, which draw on the direct experience of the tutor. The first two case
studies involve building autonomous intelligent agents (both virtually and physically
embodied) to help autistic children develop social communication skills. The other two
case studies focus on using autonomous UAVs for surveillance applications and intelligent
rovers for space exploration.
1.3 Agent and multi-agent research methodologies
Tutor: Michael Rovatsos
This tutorial provides an introduction to the field of agents and multiagent research
methodologies and an in-depth discussion of its methodological foundations. Starting
from an overview of the history and state of the art in the field, we will review main research methods, approaches to evaluating agents research, and provide guidance for plan-
2
1 Theme: Agents, from research to applications
ning, structuring, and conducting high-quality research projects so as to avoid methodological pitfalls and maximise impact. Moreover, the tutorial will provide space and
time for method reflexion and debate on different approaches to agents research, and for
“taking stock” of the state of the field. The primary target audience of this tutorial are
doctoral students, researchers new to the field, and practitioners who are interested in
using agents methods in their applications. No specific technical background is required
apart from basic algebra, logic, probability theory, and familiarity with formal notation.
3
2 Theme: Agent systems development
2 Theme: Agent systems development
2.1 Agent-oriented programming
Tutors: Raphael Bordini, Mehdi Dastani, Koen Hindriks and João Leite
With the significant advances in the area of autonomous agents and multi-agent systems in the last few years, promising technologies have emerged as a sensible alternative
for the development and engineering of multi-agent systems. The result is a variety
of programming languages, execution platforms, and tools that facilitate the development and engineering of multi-agent systems. This course aims at introducing novices,
researchers, and developers, who already have a basic understanding of multi-agent systems, an overview of the programming languages, techniques, and tools that are currently available to support the effective implementation of multi-agent systems. In this
course participants will also learn and practice some basic skills in developing multi-agent
systems. This course will present and discuss various issues in building multi-agent systems focusing on the aim and characteristics of various agent-oriented programming
languages and how multi-agent applications can be implemented using these programming languages. The programme of this course will include: a very brief presentation
of agent-oriented software engineering; an overview of the research agenda in programming languages for multi-agent systems; description and discussion of various well-known
agent languages such as 2APL, 2OPL, Jason, GOAL, Jadex, Jade, Jack and Minerva;
and some typical agent-based applications. This course will be accessible for beginners
but also interesting for more advanced students in the area of agent-oriented programming. It requires knowledge of some basic notions of BDI agents and logic programming.
A hands-on approach will be used to allow participants exercise basic skills to implement
a multi-agent system by means of a well-structured exercise. For this purpose, we will
present and explain the agent-oriented programming language Goal in sufficient details to
complete a programming exercise on a search and retrieval task using the Blocks World
for Teams (BW4T).
2.2 Agent-based modelling and computational economics
Tutors: Steve Phelps and Neil Rayner
Traditional economic models of market places are typically static in nature and are
unable to explain many of the properties of empirical data from real-world exchanges.
An alternative research paradigm called Agent-Based Computational Economics (ACE)
attempts to explain these properties by analysing simulations of marketplaces populated by agents - autonomous computer programs which adapt their trading behaviour
in response to changing market conditions. This tutorial will provide an overview of
agent-based computational economics, and a hands-on introduction to the JASA software toolkit which can be used to analyse several different models of financial markets
from the agent-based modelling literature.
4
2 Theme: Agent systems development
2.3 Normative multi-agent systems
Tutor: Wamberto Vasconcelos
Norms have been used to represent desirable behaviours that software agents should exhibit in sophisticated multi-agent solutions. Normative multi-agent systems incorporate
explicit representations of norms – these are the obligations, prohibitions and permissions
associated to individual and groups of agents as they come together to act and interact
(collaborating or competing) to achieve individual and collective goals. We have a mature, and growing, wealth of research, results, tools and techniques exploring normative
aspects within multi-agent systems. This tutorial will present the underpinnings of norms
(namely, deontic logics), alternative representation formalisms and their semantics, and
how norms have been explored in the design, analysis, verification, provisions for infrastructure, norm-aware agency theories (including norm-based agent programming), and
agent-oriented software engineering.
5
3 Theme: Agreement technologies
3 Theme: Agreement technologies
3.1 Voting theory
Tutor: Ulle Endriss
Voting theory studies methods for electing a best option from a set of alternatives, given
the preferences of the members of a group. While traditionally concerned with political
elections, voting theory is equally relevant to collective decision making in multiagent
systems. This is one reason why voting has recently become such a popular research topic
in AI. The other reason is that many of the tools and techniques of AI have proven useful
for the analysis of voting rules (for example, complexity theory, knowledge representation,
and automated reasoning). This tutorial will provide an introduction to the theory of
voting. We will present the most important voting rules and cover some of the classical
theorems in the field. We will also see examples for recent work in computational social
choice, which brings together ideas from social choice theory (including voting theory)
and computer science (including AI). No specific background knowledge will be assumed;
the tutorial will be accessible to everyone attending EASSS.
3.2 Dialogue
Tutor: Simon Parsons
This course introduces the topic of dialogue between multiagent systems, concentrating
on dialogues that are based around the exchange of arguments. The course introduces the
basic ideas of argumentation-based dialogue and then fills in the formal details needed
to specify a dialogue model — communication language, protocol, and semantics. The
course includes a short introduction to formal argumentation to make it self-contained,
and will include audience participation and interaction.
3.3 Negotiation
Tutor: Nicolas Maudet
This course is divided into three lectures. The first lecture will be dedicated to the
basics of bilateral negotiation, including axioms for negotiation and classic protocols. In
the second lecture, we shall discuss multilateral negotiation, that is, negotiation involving
more than two agents. Finally, the third lecture will introduce and illustrate the notion of
argumentation-based negotiation. Here, agents can use argumentation techniques during
the negotiation, e.g., to justify their proposals and try to convince others.
6
4 Theme: Game theory
4 Theme: Game theory
4.1 Game theory for computer science
Tutor: Michael Wooldridge
If we aim to build artificial agents that must act in environments populated by other
artificial agents, then it is very natural to ask what mathematical models and concepts we
can use to understand the interactions that might take place between such agents. Game
theory is one answer to this question. Game theory is a branch of micro-economics which
attempts to model and understand rational interaction between self-interested agents.
This tutorial is aimed at students who want to learn what game theory is about, why it
is relevant for AI researchers in general and multi-agent systems researchers in particular.
Assuming no prior knowledge of game theory, the tutorial starts by motivating the role
of game theory in AI, introduces the student to the key concepts of game theory (both
non-cooperative and cooperative), the key solution concepts for non-cooperative games,
and the various game theoretic models used by AI researchers.
4.2 Reasoning about cooperation
Tutors: Nils Bulling and Paolo Turrini
In this tutorial we show how one can model and reason about the power of groups of
agents (i.e.coalitions) in game-like scenarios. We start with some standard normal form
games, and look at the power of a coalition in a game in terms of what that coalition
can achieve by strategy coordination in that game. Then we define abstract models
of power associating to a coalition abstract properties that the coalition can achieve
(the so-called effectivity models), and show under what assumptions the abstract models
correspond to the simple models. In the second part of the lecture we discuss logical
languages to talk about individual and coalitional effectivity, namely coalition logic (CL)
and alternating-time temporal logic (ATL). Building up on our previous observations, we
show how formulas of CL and ATL can be used to specify intuitive properties of agents
in game-like scenarios. We also present axiomatic systems of CL and ATL. Then, we
conclude with a brief exposition of model checking for ATL, and the way it can be used
to reason about particular models.
7
5 Theme: Planning for agents
5 Theme: Planning for agents
5.1 Introduction to AI planning
Tutors: Amanda Coles and Andrew Coles
In order to function autonomously, intelligent agents need to be able to plan a course
of action to reach their desired goals. This tutorial introduces the established research
area of artificial intelligence (AI) planning, focusing in particular on domain-independent
planning, which is concerned with creating planners, systems capable of producing plans
for different situations given an abstract model of the world. This model comprises an
initial state, a goal state and a description of the actions an agent can apply. Since they
are not limited to a single application, such systems have a wide range of applicability in
the real world from controlling robotic agents and transportation networks to large-scale
factory control. We discuss real-world applications of planning throughout the tutorial
to showcase its applicability and ground the theoretical ideas presented. We begin by
introducing the standard input language used by planners, PDDL, allowing participants
to see how a planning problem is represented. We discuss the techniques used by planning
systems to generate plans, including AI search techniques and heuristic guidance, showing
how this is automatically extracted from the problem in a domain-independent manner.
We go on to discuss how to plan with more expressive world models involving concurrent
temporal actions and continuous numeric change. At the end of the tutorial attendees
will have an appreciation of the basics of AI planning research and will be equipped to
make use of AI planners to solve problems in new settings.
5.2 Decision making under uncertainty
Tutors: Matthijs Spaan and Frans Oliehoek
Decision making is an important skill of autonomous agents. This tutorial focuses on
decision making under uncertainty in sensing and acting, common in many real-world
systems. In particular, we will be concerned with planning problems that optimize how
an agent should act given a model of its environment and its task. Many of such planning
problems can be formalized as Markov decision processes (MDPs) or extensions of this
model.
The tutorial will give an introduction of the MDP model and some of its standard
solution methods. Subsequently, it will extend the decision making problem to deal with
noisy and imperfect sensors, known as partially observable MDPs (POMDPs). As agents
often do not exist in isolation, attention will be given to the problem of decision making
under uncertainty with multiple, interacting agents. Finally, several emerging topics in
single and multiagent decision-making under uncertainty will be highlighted.
8
6 Theme: Verification of multi-agent systems
6 Theme: Verification of multi-agent systems
6.1 Model checking multi-agent systems
Tutors: Alessio Lomuscio and Franco Raimondi
In the last ten years tools and techniques for model checking multi-agent systems have
evolved from simple prototypes handling a few dozens of states to products now capable
of handling state spaces of size 1020 and above. This tutorial will cover the theoretical
foundations of multi-agent system verification, with a particular focus on symbolic model
checking algorithms using Ordered Binary Decision Diagrams for temporal, epistemic,
and strategic modalities. In addition to the standard fix-point characterisation of these
modalities, we will also present the issues associated with verification under fairness and
counterexample/witness generation. The tutorial will also include hands-on session on
the model checker MCMAS. The overall objective is to enable students to achieve a good
understanding of this domain and provide pointers to possible research directions and
open problems.
6.2 Verifying agent-based autonomous systems
Tutors: Louise Dennis and Michael Fisher
While the idea of "Autonomous Systems" is both appealing and powerful, actually
developing such systems to be reliable is far from simple. An important aspect is to be
able to verify the truly autonomous decision-making that forms the core of many such
systems. In this tutorial, we will introduce a particular approach to the formal verification of agent-based autonomous systems. This will incorporate material on practical
autonomous systems, agent programming languages, formal verification, agent modelchecking, and the practical analysis of autonomous systems.
9
7 Tutors: biographical sketches
7 Tutors: biographical sketches
Sara Bernardini
(King’s College London, United Kingdom)
Sara Bernardini is a research associate in the Planning Group at King’s College London.
Her current research focuses on building autonomous planning-based agents for surveillance applications. In 2008 – 2012, she was a research fellow at the London Knowledge
Lab where she was part of an interdisciplinary team and contributed to building an intelligent virtual agent that helps autistic children develop social communication skills.
Previously, in 2005 – 2007, she was a research scientist at NASA Ames Research Center
where she worked on developing autonomous agents for space exploration. Sara received
her Ph.D. in Artificial Intelligence in 2008 and her research interests include autonomous
and intelligent agents, automated planning, cognitive architectures, and knowledge representation and engineering.
Raphael Bordini
(Federal University of Rio Grande do Sul, Brazil)
Rafael Bordini is an associate professor at the Institute of Informatics, Federal University
of Rio Grande do Sul (INF-UFRGS). He obtained a PhD in Computer Science from
University College London in 1999, then was a visiting professor at INF-UFRGS until
2002 when he moved to the University of Liverpool as a post-doc research fellow. In
2004 he joined Durham University as a Lecturer in Computer Science, a position he
held until 2009 when he moved back to INF-UFRGS. His main research interests are
in programming languages and verification techniques for autonomous software systems,
particularly multi- agent systems. He has published over 80 papers in international
venues, and has co-edited 5 books as well as co-authored a book on the Jason multiagent development platform published by Wiley. He has been PC or senior PC member
in many editions of Agents and AI conferences, including AAMAS, IJCAI, and ECAI, and
has been PC member for a number of events including ProMAS, DALT, LADS, CLIMA,
MABS, MoChArt, and JELIA to name just a few. He was member of the organising
committee of two AAMAS editions, and has organised MALLOW-2007 as well as various
editions of the ProMAS workshop. He has given tutorials on multi-agent programming
at AAMAS, EASSS, and the DALT Spring School. He is a member of the IFAAMAS
board of directors, emeritus member of the EURAMAS board of directors, member of
the EPSRC (UK) Peer Review College 2006-2009 and 2010-2013, and member of the
Steering Committee of ProMAS.
Nils Bulling
(Clausthal University of Technology, Germany)
Since 2011, Nils Bulling is assistant professor at Clausthal University of Technology,
where he also received his PhD in 2010. His research is centered around foundations
of multi-agent systems, and in particular around logical and game-theoretic approaches.
In the last years he has given (international) lectures related to theoretical foundations
of computer science, multi-agent systems and verification of systems. Dr. Bulling has
served on many program committees for international events. He has been refereeing
10
7 Tutors: biographical sketches
papers and articles for journals, workshops, and conferences, and is regularly publishing
papers. He has also co-authored two book chapters on the specification and verification
of multi-agent systems and on strategic reasoning in multi-player games.
Mehdi Dastani
(Utrecht University, The Netherlands)
Mehdi Dastani is Associate Professor in computer science at the Utrecht University. He
is working in the area of multi-agent systems and multi-agent program- ming and has
published many papers on these subjects. He has been involved in the design, development, and extensions of BOID, 3APL, 2APL, and 2APLlanguages which can support the
implementation of multi-agent systems. He has given many courses on these subjects,
among which Intelligent Information Agent, Multi-agent systems, and Multi-Agent Programming. He is a member of the steering committee of the ProMAS (The International
Workshop on Pro- gramming Multi-Agent Systems: Languages and Tools). He is the
PC member of various international conferences and workshops such as AAMAS, IJCAI,
ECAI, JELIA, and MATES. He has edited, together with the ProMAS organ- isers, two
books on Programming Multi-Agent Systems published by Springer.
Ulle Endriss
(University of Amsterdam, The Netherlands)
Ulle Endriss is Associate Professor of Logic and Artificial Intelligence at the Institute
for Logic, Language and Computation (ILLC) at the University of Amsterdam, where
he carries out research at the interface of computational logic, artificial intelligence, and
economic theory. He is an associate editor of the Journal of Autonomous Agents and
Multiagent Systems and an elected member of the IFAAMAS Board of Directors, and
he served as programme co-chair of EASSS-2011 in Girona. Ulle received his PhD from
King’s College London in 2003.
Koen Hindriks
(Delft University of Technology, The Netherlands)
Koen Hindriks is Assistant Professor at the Computer Science Department at the Delft
University of Technology. His research interests are focused in particular on agentoriented programming using common sense concepts like beliefs and goals, verification
and specification of such agents, and, more application- oriented, on negotiating software
agents. He has worked on and helped develop several agent programming languages
such as 3APL and GOAL. He teaches various courses on multi-agent systems, including a course titled ”Multi-Agent Systems”, a project called ”Programming Multi-Agent
Systems”, and a master course ”Artificial Intelligence Techniques”. He has co-organised
the Eleventh International Workshop on Cooperative Information Agents (CIA’07) and
has organized the Sixth International Workshop on Programming Multi- Agent Systems
(ProMAS’08).
11
7 Tutors: biographical sketches
João Leite
(New University of Lisbon, Portugal)
João Leite is Assistant Professor at the Computer Science Department at the New University of Lisbon. His research interests focus on Knowledge Representation and NonMonotonic Reasoning with Logic Programming for Multi-Agent Systems. He has been
involved in the design of several extensions of Answer Set Programming for Agent Systems, such as EVOLP, DLP, MDLP and KABUL, and the logic programming based agent
architecture Minerva. He has co-organised the Third, Fourth, Fifth, Eleventh and Twelfth
International Workshops on Computational Logic in Multi-Agent Systems (CLIMA III,
CLIMA IV, CLIMA V, CLIMA XI and CLIMA XII), the First and Second Workshops
on Declarative Agent Languages and Technologies (DALT’03 and DALT’04),the First,
Second and Third International Workshop on Languages Method- ologies And Development Tools for Multi-Agent System (LADS007, LADS’009 and LADS’010) and was
Conference Chair of the Ninth International Conference on Logics in Artificial Intelligence (JELIA’04), held in Lisbon, Portugal, in 2004 and Organiser of the 10th European
Agent System Summer School (EASSS’08) held in Caparica, Portugal in May 2008. He
has authored one book, edited several books published by Springer and three journal
special is- sues, and co-authored more than 60 scientific papers published internationally
(cf. http://centria.di.fct.unl.pt/ jleite for details).
Alesio Lomuscio
(Imperial College London, United Kingdom)
Alessio Lomuscio is Professor in Logic for Multi-Agent Systems at Imperial college London. His research concerns the logic-based specification and verification of autonomous
systems.
Michael Luck
(King’s College London)
Michael Luck is Professor of Computer Science and Deputy Head of the School of Natural
and Mathematical Sciences at King’s College London. He was Head of the Department
of Informatics from 2011 to 2013, where he also works in the Agents and Intelligent
Systems group, undertaking research into agent technologies and intelligent systems. He
is Scientific Advisor to the Board for Aerogility. His work has sought to take a principled approach to the development of practical agent systems, and spans: formal models
for intelligent agents and multi-agent systems; formalisation of existing practical agent
systems and theories; information-based agent applications in domains such as genome
analysis; norms and institutions; trust and reputation; agent infrastructure; declarative
programming of agent systems; agent-oriented software engineering; application to Grid
computing; and industrial deployment and technology forecasting.
Professor Luck has published over 200 articles in these and related areas, and twelve
books (including monographs, textbooks, and edited collections); he was lead author of
the AgentLink roadmaps in 2003 and 2005. He is a a director of the board of the International Foundation for Autonomous Agents and Multi-Agent Systems (IFAAMAS),
co-founder of the European Multi-Agent Systems (EUMAS) workshop series, and a Steering Committee member for the Central and Eastern European Conference on Multi-
12
7 Tutors: biographical sketches
Agent Systems (CEEMAS). Professor Luck was a member of the Executive Committee of
AgentLink III, the European Network of Excellence for Agent-Based Computing, having
previously been the Director of AgentLink II. He is an editorial board member of Autonomous Agents and Multi-Agent Systems, the International Journal of Agent-Oriented
Software Engineering, Web Intelligence and Agent Systems, and ACM Transactions on
Autonomous and Adaptive Systems. He was also general co-chair of the Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), held
in Toronto, Canada in May 2010.
Nicolas Maudet
(Univ. Pierre et Marie Curie, France)
Nicolas Maudet is a Professor of Computer Science at Univ. Pierre et Marie Curie, Paris.
His field of research is AI, more specifically multi-agent systems. He has been working on
collective decision-making issues for more than 10 years, with contributions in particular
in computational social choice and multi-agent argumentation. He is an Associate Editor
of the Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS), and has been
involved in several previous EASSS editions.
Simon Parsons
(City University of New York, USA)
Simon Parsons is Professor of Autonomous Systems at University of Liverpool. Simon got
his PhD in 1993 from the University of London, and took his first faculty job there two
years later. He worked at the University of London, the University of Liverpool and MIT
before moving to City University of New York in 2002. He returned to the University of
Liverpool in July 2013. Simon’s research interests center on multiagent systems, including
the areas of argumentation, agent interaction, and decision-making, and he has published
over 200 papers and written or edited 10 books on these topics. Dr Parsons currently
holds grants from the National Science Foundation, the Army Research Laboratory, and
the National Security Agency. He is the co-editor of Knowledge Engineering Review,
and an Editorial Board member of the Journal of Autonomous Agents and Multiagent
Systems.
Steve Phelps
(University of Essex, United Kingdom)
Steve Phelps’ research focuses on the application of agent-based simulation to economic analysis, and encompasses topics crossing several disciplines: finance (trading
strategies & market structure), economics (game theory, auction theory, mechanism design) and computer-science/complexity (multi-agent reinforcement-learning, design of coevolutionary algorithms, complex adaptive systems). He worked on the EPSRC-funded
Market-based Control of Complex Computational Systems project developing a novel
technique for market design (evolutionary mechanism design) in which he applied techniques from agent-based modelling, evolutionary computation and heuristic optimization
to the mechanism design problem. More recently, he has been applying agent-based modelling and evolutionary game-theory to the study of social networks and the emergence
of cooperation via indirect reciprocity.
13
7 Tutors: biographical sketches
Franco Raimondi
(Middlesex University, United Kingdom)
Franco Raimondi is a Senior Lecturer in Computer Science at Middlesex University,
London. His research applies logic-based methods to the formal verification of complex
and critical systems.
Neil Rayner
(University of Essex, United Kingdom)
Neil is currently conducting research into the modelling of high-frequency financial market
time series data. Specifically he is using agent-based models to model the empirically
observed stylized facts of double auction markets, such as the London Stock Exchange
order book, which trades the FTSE100, FTSE250 and other securities (SETS) . He
worked for seven years at Clearsteam Bank, an International Central SecuritiesDepository
(ICSD) and Clearing House for European securities, on the development of its clearing
and settlement platform (Creation). Recently he received an MSc in financial software
engineering and he is now completing a PhD in computational finance. He has previously
presented papers at the Trading Agent Design Analysis Workshop 2011 at IJCAI, and
the ICEC 2011 session on Robustness and Reliabilityof Electronic Marketplaces.
Michael Rovatsos
(University of Edinburgh, United Kingdom)
Michael Rovatsos is a Lecturer at the School of Informatics of the University of Edinburgh, where he leads the Agents Group within the Centre for Intelligent Systems and
their Applications. He holds a PhD in Informatics from the Technical University of Munich (2004), and a Diploma in Informatics from the University of Saarbruecken (1999).
His research is in agents and multiagent systems, and he has (co-)authored over 60 papers
in this area. He has in-depth knowledge of the international agents research landscape:
He has published and presented papers at all but one of the previous AAMAS conferences
since 2002. He has taught University-level courses on agent-related topics for seven years,
engaged in several extended research visits to international research groups in the area,
and has been actively involved in the organisation of over 40 international agent-related
events.
Paolo Turrini
(Imperial College London, United Kingdom)
Paolo Turrini is a reasearch associate at the University of Luxembourg. He obtained
his PhD from Utrecht University with a thesis on logic and coalitional game theory.
He works in the field of multi-agent systems on topics such as logic and games, norms,
bounded rationality. He serves as a PC member of the main conferences as well as smaller
workshops in MAS and AI (AAMAS, IJCAI, and others). He has recently been awarded
an Intra European Marie Curie Fellowship, and from April 2013 he will be a research
associate at the Department of Computing, Imperial College London. For more details,
check http://icr.uni.lu/paolo/.
14
7 Tutors: biographical sketches
Wamberto Vasconcelos
(University of Aberdeen, United Kingdom)
Dr. Vasconcelos holds a PhD from the University of Edinburgh (UK, 1995). He worked
as a lecturer and/or as researcher in Edinburgh, Brazil and Switzerland, before joining
the Department of Computing Science, University of Aberdeen, in 2002, where he is
currently a Senior Lecturer. His main research interests are logics and (multi-agent
system) software engineering. He has carried out research on representation of norms
and reasoning with and about norms, to make agents norm-aware, and to explore norms
in the verification and analysis of multi-agent systems, as well as in other stages of multiagent systems engineering. He has served in the (senior) programme committee of many
versions of IJCAI, AAMAS, AAAI, ECAI, ICAIL, and many other smaller events and
workshops. He has co-authored, over 100 books, articles and scientific papers on various
aspects of (normative) multi-agent systems.
Michael Wooldridge
(University of Oxford, United Kingdom)
Michael Wooldridge is a Professor of Computer Science in the Department of Computer
Science at the University of Oxford, and a Senior Research Fellow at Hertford College.
Michael joined Oxford on 1 June 2012; before this he was for twelve years a Professor
of Computer Science at the University of Liverpool. In October 2011, he was awarded
a 5-year ERC Advanced Grant, entitled "Reasoning About Computational Economies"
(RACE).
Prof. Wooldridge is a AAAI Fellow, an ECCAI Fellow, an AISB Fellow, and a BCS
Fellow. In 2006, he was the recipient of the ACM Autonomous Agents Research Award.
In 1997, Michael founded AgentLink, the EC-funded European Network of Excellence
in the area of agent-based computing. He was program chair for the 19th European
Conference on Artificial Intelligence (ECAI-2010), held in Lisbon, Portugal, in August
2010. Michael will also be General Chair for the 24th International Joint Conference on
Artificial Intelligence (IJCAI-2015), to be held in Buenos Aires, Argentina.
Between 2003 and 2009 Michael Wooldridge was co-editor-in-chief of the Journal Autonomous Agents and Multi-Agent Systems. He is an associate editor of the Journal
of Artificial Intelligence Research (JAIR) (2006-2009, 2009-2012), an associate editor of
Artificial Intelligence journal (2009-2012) and serves on the editorial boards of the Journal of Applied Logic, Journal of Logic and Computation, Journal of Applied Artificial
Intelligence, and Computational Intelligence.
15