Teaching material available here

Time Mon Tue Wed Thu Fri
08:30 Registration Registration
09:00 SMODON
OptiMAS SpatialMAS
10:00 Registration
10:30 EASSS
Break Break Break Break
OptiMAS SpatialMAS
13:00 Lunch Lunch Lunch Lunch Lunch
14:30 CERMAS
Social Program AgIoT
16:00 Break Break Break
16:30 CERMAS


[FMAS] Foundation of Multi-Agent Systems

Prof. Andrea Omicini



[AgIoT] Agents meet the IoT: Towards Ecosystems of Networked Smart Objects

Prof. Giancarlo Fortino

Abstract.The future Internet of Things (IoT) will enable a new and wide range of decentralized systems (e.g. from smart homes to smart cities) where “things”, able to sense/actuate, compute and communicate with other machines and with humans, will play a central role. The growing importance of such novel cyberphysical network and technology demands suitable and effective paradigms able to fulfill the general and specific requirements of IoT systems engineering. In this tutorial, we propose the exploitation of the agent-oriented computing paradigm to support IoT systems analysis, design, and implementation. The synergic meeting of Agents with the IoT will make it possible the development of dynamic IoT systems of diverse scales. First, we introduce background and literature about IoT, with a specific focus on IoT systems development along with currently available agent-oriented approaches. Then, we present in detail our agent-oriented approach specifically based on the ACOSO (Agent-based COoperating Smart Objects) Methodology and related middleware, that provides an effective agent programming model and an agent execution heterogeneous platform along with ad-hoc IoT tools for the construction of an IoT system in terms of a Multi-Agent System. A case study concerning the development of a “Smart University” system is described to show the flexibility and effectiveness of the proposed approach. Finally, future challenges will be delineated towards cloud-assisted agent-based approaches for IoT.

Short CV. Giancarlo Fortino is (Habilitated Full) Professor of Computer Engineering at the Department of Electronics, Informatics and Systems (DEIS) of the University of Calabria (Unical), Rende, Italy. He received a Laurea (BSc+MSc) degree and a PhD in Computer Engineering from Unical, in 1995 and 2000, respectively. He is Adjunct Prof. at Wuhan University of Technology and Adjunct Senior Research Fellow at Italian National Research Council. He has also been a visiting researcher at the International Computer Science Institute, Berkeley – USA, in 1997 and 1999, and a visiting professor at the Queensland University of Technology, Australia. His research interests include distributed computing and networks, agent-based computing, wearable computing, wireless sensor networks, IoT technology. He is author of over 250 papers in int’l journals, conferences and books. He currently serves in the editorial board of IEEE T. HMS, IEEE T. Aff Comp, JNCA, EAAI, INFFUS. He is also co-founder and CEO of SenSysCal S.r.l., a spin-off of Unical, whose mission is the development of innovative systems and services based on IoT technology.

Contact info




[CERMAS] Complex Event Recognition in Multi-Agent Systems

Dr. Alexander Artikis

Abstract. In multi-agent systems, there is a paramount need to deal efficiently with large event streams. For instance, agents need to infer the (local) state(s) of a system current at each time, given the exchanged messages and the state of the environment, as well as detect violations of the rules of the game. In an electronic marketplace with thousands of members, for example, it is necessary to compute in real-time the pending contracts and commitments, given the actions of the agents (bids, offers, and so on). Real-time reasoning in large multi-agent systems may be achieved by means of complex event recognition techniques. Complex event recognition systems accept as input a stream of time-stamped simple events, such as the events coming from a sensor or emitted by an agent. Using simple events as input, event recognition systems identify complex/composite events of interest—collections of events that satisfy some pattern, such as collusion in an auction. In this tutorial, we will present how complex event recognition techniques may be used for executing efficiently the specifications of very large multi-agent systems. Moreover, we will show how probabilistic event recognition supports robust inference in the presence of noisy communication channels and imperfect sensors. Finally, we will demonstrate how machine learning techniques enable the automated construction of complex event patterns expressing a multi-agent system specification.

Short CV. Dr. Alexander Artikis is a Lecturer in the University of Piraeus, and the leader of the Complex Event Recognition lab (http://cer.iit.demokritos.gr) of NCSR Demokritos’, in Athens, Greece. He holds a PhD from Imperial College London on multi-agent systems, while his research interests lie in the areas of artificial intelligence and distributed systems. Alexander has been working on several international projects; he is the scientific coordinator of the SPEEDD project (http://speedd-project.eu).

Contact him at http://users.iit.demokritos.gr/~a.artikis/


[SMODON] Simple Models of Opinion Dynamics On Networks. The role of network structure on the emergence of consensus in simple models of opinion dynamics.
Vincenzo Nicosia. 
Abstract. The formation of consensus in a social system is a process which underlies many human activities including, among the others, the choice of our governors through democratic elections, the emergence of cultures and sub-cultures, and the massive mobilisation of people supporting a common cause. Hence, studying how and why consensus can emerge in a population as a result of the local interaction among agents is of pivotal importance to understand societies and their transformations. Complexity science has tackled the problem of opinion formation from different angles, and one of the most interesting approach is that proposed in the field of network science, according to which the emergence of global consensus in a population of agents is intimately connected with (and largely determined by) the structure of the network of interactions among those agents. In this tutorial we will provide a short introduction to a few fundamental concepts of complex network theory, and we will review several mathematical models, with minimal assumptions about the dynamics of agent-to-agent interaction, which are able to explain the emergence of consensus in a society of interconnected agents. We will then provide an overview of recent results which show how the dynamics of consensus formation is affected (sometimes in a radical way) by the presence of several layers of inter-agent interaction, represented as a multi-layer graph.
Short CV. Vincenzo Nicosia got a PhD in Computer Science from the University of Catania in 2008. Then, he held several post-doctoral research positions at the University of Catania (Department of Mathematics, Department of Physics), at the Scuola Superiore di Catania (Laboratory for Complex Systems), at the University of Cambridge (Computer Laboratory), and at Queen Mary University of London (School of Mathematical Sciences). Since November 2015, he is a Lecturer (Assistant Professor) in Network and Data Analysis, in the Complex Systems and Networks Group, School of Mathematical Sciences, QMUL.
Contact info. 
Vincenzo Nicosia
School of Mathematical Sciences
Queen Mary University of London
Mile End Road, E1 4NS
London (UK)
email: v.nicosia@qmul.ac.uk
web: http://www.maths.qmul.ac.uk/~vnicosia/

[MODeM] Multi-objective decision making.

Shimon Whiteson and Diederik M. Roijers. 

Abstract. Many real-world tasks require making decisions that involve multiple possibly conflicting objectives. To succeed in such tasks, intelligent systems need planning or learning algorithms that can efficiently find different ways of balancing the trade-offs that such objectives present. In this tutorial, we provide an introduction to decision-theoretic approaches to coping with multiple objectives. We first present an overview of multi-objective decision problems, with real-world examples. Then, we show that different assumptions about these problems lead to different solution concepts such as the convex hull and the Pareto front. Next, we provide an overview of state-of-the-art algorithms for tackling them, such as multi-objective variants of dynamic programming and linear programming approaches. Finally, we highlight some applications of multi-objective methods and discuss some of the most important open questions.

Short CV. 

Shimon Whiteson (https://www.cs.ox.ac.uk/people/shimon.whiteson/) is an Associate Professor in the Department of Computer Science at the University of Oxford.  His research focuses on decision-theoretic planning and learning for single and multi-agent systems. He was awarded an ERC Starting Grant from the European Research Council in 2014.

Diederik M. Roijers did his PhD on multi-objective decision-theoretic planning at the University of Amsterdam. Currently, he is working on a multi-objective approach to social robotics in the TERESA project (http://teresaproject.eu/), as a post-doctoral researcher in the Department of Computer Science at the University of Oxford.

Contact info. 

Shimon Whiteson

Diederik M. Roijers

[SpatialMAS] Spatial Multi-Agent Systems

Andrea Omicini, Stefano Mariani, Mirko Viroli
Abstract. The huge availability of geographical and spatial data, along with the impulse from ubiquitous and pervasive application scenarios, has pushed the boundaries of complex system engineering towards spatial computing. There, space (in any of the many possible acceptations of the term) represents at the same time the physical container of distributed pervasive applications, the source of a huge amount of data, information, and knowledge, and the target of both epistemic and practical actions. Agents – as the basic abstraction for distributed computing –, rational agents – as the basic units for encapsulating intelligence –, and multi-agent systems (MAS) – as the social abstraction for collective behaviours – represent the most likely candidates for providing an original framework for spatial computing coherehtly covering conceptual, technical, and methodological issues. In this survey tutorial we elaborate on the state-of-the art of spatial computing, and show how the classical ontological foundation for MAS (agents, societies, and environment) can coherently capture the essential aspects of spatial computing, also providing for original perspectives and research directions in the novel field of “Spatial MAS”.

Andrea Omicini (PhD) is Full Professor at DISI, the Department of Computer Science and Engineering of the Alma Mater Studiorum–Università di Bologna. He published over 275 articles on coordination, multi-agent systems, intelligent systems, programming languages, Internet technologies, middleware, simulation, software engineering, pervasive systems, self-organisation. On the same subjects, he edited 13 international books, and guest-edited 18 special issues of international journal or magazines. He held a number of talks and tutorials at international conferences and schools; supervised or evaluated several PhD theses, both national and international; organised and chaired many international conferences and workshops, such as AAMAS 2002, SELMAS 2002, CIA 2003, SAC 2004 & 2005, EUMAS 2006, DALT 2003, 2004, 2005 & 2010, ESAW 2000, 2001, 2003 & 2004, ITMAS 2010, CompleNet 2014, PRIMA 2015. He organised EASSS 2002 and the DALT 2011 Summer Schools, and he has been member of the EASSS Advisory Board since 2009. He worked as the Chair of the SIG on Agents and Multi-Agent Systems of the Italian Association for Artificial Intelligence (AI*IA), and the ACM Representative in the IFIP Technical Committee 12 “Artificial Intelligence”. He was Member of the Board of Director of the European Association for Multi-Agent Systems (EURAMAS) from 2009 to 2015.

Stefano Mariani (PhD) is a Post-Doc research collaborator at DISI, the Department of Computer Science and Engineering of the Alma Mater Studiorum–Università di Bologna. He earned his Master Degree in December 2011 with a dissertation titled “Molecules of Knowledge: a new approach to knowledge production, management and consumption”. He won a one-year research grant funded by the SAPERE Project under the supervision of Proff. Mirko Viroli and Andrea Omicini. He earned is Ph.D. Degree (Doctor Europeaus) in May 2016 under the supervision of Prof. Andrea Omicini, with a dissertation titled “Coordination Issues in Complex Socio-technical Systems: Self-organisation of Knowledge in MoK”. He published over 20 papers at international conferences, workshops, and journals. He supervised more than 30 theses and master theses. He is currently the main developer of the TuCSoN coordination technology and of the MoK middleware for self-organisation of knowledge.

Mirko Viroli (PhD) is Associate Professor at DISI, the Department of Computer Science and Engineering of the Alma Mater Studiorum–Università di Bologna. He is an expert of programming languages, computational models, multi-agent systems, pervasive computing, and engineering of self-adaptive and self-organising systems. On such topics, he regularly teaches courses at DISI, and has written over 250 articles. He is member of the Editorial Board of the “Knowledge Engineering Review” (Cambridge University Press), was program co-chair of ACM SAC 2008 and 2009, IEEE SASO 2014, and COORDINATION 2015. He led the University of Bologna team in the european project FP7 STREP “SAPERE” (Self-Aware Pervasive Ecosystems, 2010-2013), under call FET Proactive Intiative: Self-Awareness in Autonomic Systems (AWARENESS).


Andrea Omicini

Stefano Mariani

Mirko Viroli

[JA4MAS] Introduction to Judgment Aggregation for Multi-Agent Systems.
Marija Slavkovik. 
Abstract. Reaching some form of consensus is often necessary for  autonomous agents  that  want to coordinate their actions or otherwise engage in  joint activities. One way to reach a consensus is by aggregating individual information, such as decisions, beliefs, preferences  and constraints. Judgment aggregation is a social choice method, which generalises voting, that studies the aggregation of individual judgments regarding the truth-value  of logically related propositions. As such, judgment aggregation is applicable for consensus reaching problems in multi-agent systems. As other social choice theory, judgment aggregation research is abundant with impossibility results and axiomatic characterisations of aggregation functions. However, the aim of this tutorial is to give an introduction to the methods of judgment aggregation, not the impossibility results.  After this tutorial the participants are expected to be able to read and understand judgment aggregation literature and have a grasp on the state-of-the-art and open questions in judgment aggregation research within multi-agent systems. 
Short CV. Marija Slavkovik is a postdoctoral fellow at the University of Bergen since 2013 where she conducts research on  collective reasoning and decision making methods for artificial intelligence.  Marija obtained her doctoral degree from the University of Luxembourg with a dissertation on “Judgment Aggregation for Multi-Agent Systems” in 2012, after which she spent a year as a postdoc in the Department of Computer Science at the University of Liverpool.
Contact info. 

[NSMAS]Norm synthesis in Normative Multi-Agent Systems

Javier Morales

Abstract. When designing Multi-Agent Systems (MAS), agent interaction constitute one of the angular stones in the system. It has to be defined in such a way that agent behaviour is restricted enough so that the system meets the design requirements and yet it should be flexible enough to guarantee agents’ autonomy. Norms (together with related concepts such as laws, social conventions and rules) have proven to be an agent coordination mechanism that meets these design requirements.The research area of Normative Multi-Agent Systems studies how to endow MAS with norms. It encompasses a wide range of research approaches that go from theoretical formalisations to operational specifications down to domain specific implementations. Nevertheless, inherent MAS dynamics can become rather complex, and thus, defining norms may not be a straightforward task. Typically, the norm synthesis problem has been tackled both off-line and on-line (i.e., along system’s execution). In this tutorial we will briefly introduce Normative Multi-Agent systems. We will then present different on-line norm mechanisms from the literature such as norm emergence, norm agreement, norm detection or norm signalling. Then, we will focus on norm synthesis. Students will use a novel framework for research on norm synthesis for MAS (i.e., NormLab,http://normsynthesis.github.io/NormLab/). They will be able to see in detail an automatic on-line norm synthesis process and its application to two different domain scenarios. This work follows an agent based simulation approach and its code is publicly available. Thus, those students that preinstall it will be able to follow a hands-on activity.

Short CV. Dr. Javier Morales is a Computer Scientist that holds, since february 2016, a Ph.D. in Arti cial Intelligence from the Arti cial Intelligence Research Institute (IIIA-CSIC, http://www.iiia.csic.es/). Currently, he is a post-doctoral Marie-Curie fellow at the University of Oxford, where he works in the team of Professor Michael Wooldridge. Overall, he has published 18 scientific publications in indexed journals, books, and ranked international conferences and he has supervised both B.Sc. and M.Sc. students in the area of Normative Multi-Agent Systems. In addition to research works, he has been an assistant teacher at the University of Barcelona between 2009 and 2012, and has given several lessons for students of the Arti cial Intelligence subject (AI) in the B.Sc. in Computer Science of the University of Barcelona (UB) and the Distributed Arti cial Intelligence subject (AID) in the interuniversitary master of Arti cial Intelligence (UPC-UB-URV).

Contact info. 



[DEFARG] What is “correct” when rules admit exceptions? On default logic and formal argumentation

Truls Pedersen  and Sjur Dyrkolbotn.

Abstract. The tutorial begins with a presentation of default logic that introduces Reiter’s extension-based semantics as well as the priority-based semantics due to Brewka. The participants will be introduced to important meta-logical debates right away. In particular, we will emphasize that there are many different ways of defining semantics for default logic, catering to different intuitions and modeling needs. This, in turn, will give rise to a brief presentation of the philosophical literature on defaults, including Horty’s work on the link between default logic and moral reasoning. We then present Dung’s theory of argumentation frameworks, which can be used to give a formal representation of (potential) ways of deriving consequences from default theories. We go on to present the ASPIC+ framework, which provides a general template for generating argumentation frameworks from default theories. The so-called rationality postulates are explained, before we zoom in on the crucial question of how to lift priorities between default rules to preferences between arguments. We clarify why this question is important, and track how different ways of resolving it give rise to different ways of representing a default theory as an argumentation framework. Following up on this, we briefly mention some open research questions in formal meta-argumentation. In the end, the goal is to provide the participants with a toolbox for addressing the following fundamental question of rule-based AI: what does it mean to reason correctly with rules that admit exceptions? We conclude by showing how different variants of this question, focusing on different semantic properties, can inspire new methods for test-driven development of non-monotonic inference agents. This includes a presentation of the relationship between default logic, argumentation, and logic programming.
Short CV.  

Sjur K. Dyrkolbotn is a postdoctoral researcher in the REINS group at the Department of Philosophy and Religious Studies, Utrecht University. He works on formal approaches to responsibility in the context of artificial intelligence. Dyrkolbotn did his PhD at the Department of Computer Science, University of Bergen, Norway. He is also qualified as a Norwegian lawyer, and recently completed a PhD thesis in law at Durham Law School, Durham University, UK. He is interested in the logic and philosophy of AI (particularly agency, argumentation, and truth), as well as theories of personhood and non-traditional forms of tort and criminal liability for intelligent systems.

Truls Pedersen is a postdoctoral researcher with the Media Diversity research project at the Institute for Information Science and Media Studies at the University of Bergen. He works with gathering and analyzing online news media texts. Pedersen completed his PhD in 2014 under the supervision of Prof. Thomas Ågotnes about agreement and cooperation in multi-agent systems. He is particularly interested in systems for drawing conclusions from multiple unreliable information sources.

Sjur Dyrkolbotn:
Website: http://www.uu.nl/staff/SKDyrkolbotn
Email: S.K.Dyrkolbotn@uu.nl
Truls Pedersen:
Website: http://www.uib.no/infomedia/38591/truls-pedersen
Email: truls.pedersen@uib.no


[PCAA] Programming Conversational Agents with Akka Actors

Maíra Gatti de Bayser and Paulo Cavalin

This tutorial presents how conversational agents can be implemented as Akka Actors using a mixed-initiative dialog strategy, i.e., by combining both the system- and user-initiative in a question and answer scenario. Akka is a toolkit and runtime that implements the Actor model on the JVM and was inspired by Erlang in many ways. Akka can be used to build highly concurrent, distributed, and resilient message-driven applications. A large ecosystem of more than 250 Github projects has grown on Akka, in programming languages like Scala, Java, Groovy or JRuby. The tutorial covers topics from agents pro-activeness, natural language processing, fault-tolerance and finally an exemplar integration with a mobile app to demonstrate the Cognitive Investment Advisor Application, including demo and code.

Short CV. 
Dr. Maíra Gatti de Bayser is a Research Staff Member (RSM) in the Social Data Analytics group at IBM Research, Brazil, since 2011. Maíra holds Ph.D. (2009) and M.Sc. (2006) in Software Engineering for Multi-Agent Systems from Informatics Department of the PUC-Rio, Pontifical Catholic University of Rio de Janeiro, Brazil. She has been a research fellow at the Agent and Intelligent Systems of Kings College London, England (2009), and before a visit student at the University of Waterloo, Canada (2008).

Dr. Paulo R. Cavalin is a Research Staff Member (RSM) in the Social Data Analytics group at IBM Research, Brazil. He received a Ph.D. degree in automated production engineering from École de Technologie Supérieure (ÉTS) – Université du Québec in 2011 and M.Sc. degree in applied informatics from Pontifícia Universidade Católica do Paraná (PUCPR), in 2005. Since he joined IBM in 2012, he has been conducting both theoretical and applied research in pattern recognition, machine learning, and computer vision.

Maíra Gatti de Bayser – http://researcher.watson.ibm.com/researcher/view.php?person=br-mgdebayser
Paulo R. Cavalin – http://researcher.watson.ibm.com/researcher/view.php?person=br-pcavalin



[VAAMAS.] Verification of Autonomous Agents and Multiagent Systems. 

Natasha Alechina and Brian Logan

Abstract. There is increasing interest in the application of autonomous intelligent systems technology in areas as diverse as driverless cars, UAVs, manufacturing, healthcare, personal assistants, etc. However for agent technology to be adopted in such areas, the public and regulators must be convinced that the (multi)agent programs controlling such autonomous systems are safe and reliable. This tutorial explores how state of the art agent verification techniques can be used to provide guarantees about the behaviour of a (multi)agent system. The tutorial covers both the theoretical foundations of agent and multiagent verification (temporal and epistemic logic, logics of strategic ability) and practical aspects of verification (formulating and encoding the agent model, advantages and disadvantages of different verification approaches and tools), and includes a number of worked examples using freely available model-checking tools. The tutorial assumes knowledge of basic logic.

Short CV. 

Dr. Natasha Alechina is an associate professor in the School of Computer Science, University of Nottingham. Her research interests are in applications of logic to artificial intelligence and computer science, in particular automated verification and logics for resource-bounded agents. She has published over 100 papers in leading international journals and conferences in artificial intelligence and logic. She has served on the PC or SPC of AAMAS from 2008 to 2016. She has been the lead investigator on several research projects on verification of agent systems.

Dr Brian Logan is an associate professor in the School of Computer Science, University of Nottingham. His research interests span the specification, design and implementation of multi-agent systems, including software architectures for multi-agent systems, logics for reasoning about agent-based systems, and software tools for building multiagent systems. He has published over 100 papers in leading international journals and conferences in artificial intelligence and multi-agent systems. He has served on the PC or SPC of AAMAS from 2008 to 2015, and in 2016 was an Area Expert SPC for verification and validation. He is a member of the steering group of the UK Network on Verification and Validation of Autonomous Systems.

Natasha Alechina: http://www.cs.nott.ac.uk/~psznza/
Brian Logan: http://www.cs.nott.ac.uk/~pszbsl/


[MoD-AOSE] Model Driven Agent-Oriented Software Engineering

Nikolaos Spanoudakis

Abstract. Agent Oriented Software Engineering (AOSE) emerged immediately after the autonomous agents and multi-agent systems was established as a research field of the computer science/artificial intelligence discipline. The new characteristics and concepts of multi-agent systems and autonomous agents needed to be integrated in a software engineering methodology. AOSE came to cover this need. Until today, a number of methodologies have been proposed each supporting different styles of agent programming and different agent architectures. In this tutorial, we present several AOSE methodologies found in the literature. Moreover, we present important works in the area of modeling inter-agent protocols, as one of the major issues in Agent Oriented Software Engineering (AOSE) is the modeling, representation and implementation of agent interaction protocols. Model-driven engineering (MDE) is a software engineering paradigm, that uses models at different levels of abstraction. The different phases are marked by models’ completions and transition between phases is done using model transformations (thus increasing automation). Higher-level models are transformed into lower level models until the executable system model. We will demonstrate how MDE influences an AOSE methodology looking closer at ASEME, a model driven, hybrid AOSE methodology.

Short CV. 

Nikolaos Spanoudakis (PhD) is a researcher and teaching assistant in the School of Production Engineering and Management at the Technical University of Crete (TUC), Greece. His research interests concern intelligent agents and multi-agent systems, agent-oriented software engineering, software methodologies and AI applications. Nikolaos has a PhD in computer science from the Paris Descartes University (France). He also has an MSc in Business Administration from the Technical University of Crete and a Diploma in Computer Engineering and Informatics from the University of Patras. He’s a senior member of IEEE and a member of ACM, the Hellenic Artificial Intelligence Society (HAIS), and the Technical Chamber of Greece (TCG). He has professional experience as information systems architect, designer and developer, technical manager, project manager and Information Technology and Services consultant.


Nikolaos Spanoudakis
Tel. : +30 28 21 03 77 44
Email: nikos@science.tuc.gr
www : http://users.isc.tuc.gr/~nispanoudakis




[BiDIBots] Programming Autonomous Robots using PROFETA and the BDI model

Corrado Santoro

Abstract. The tutorial has the objective to show how to program the behaviour of an
autonomous robot using the PROFETA platform. This platform is a Python-based software tool implementing the BDI model by means of a dialect of AgentSpeak(L). The tutorial will start explaining the basic problems of robot programming, highlighting the important differences between the world of autonomous (software) agents and the world of autonomous robots. Then, since a real robot will be used in the tutorial, an overview of the hardware/software architecture of the robot itself will be given, together with a description of the API provided to perform interaction with sensors and actuators.The next part will be devoted to explain how to program the behaviour of a robot using the PROFETA platform. To this aim, first an overview of the BDI model is provided; then, PROFETA is introduced, showing the basic entities, the API, the syntax and providing some usefull (theoretical) examples. The last part will present some case-studies and their solutions using PROFETA. The solutions will be tested on the real robot, thus showing the
practical functioning of the tool with its features and advantages. If there is enough time, one or more exercises will be proposed to the audience and the provided solutions will be tested on the robot. The tutorial is strongly application-oriented, however it includes some theoretical parts which serve as the basics to understand the underlying model and the working scheme of the software platform used. The tutorial is for any computer science or computer engineering PhD student or post-doc. The sole prerequisite is the knowledge of the Python language.

Short CV 

Corrado Santoro received a Master Degree in Computer Engineering in 1997 at the Università degli Studi di Catania, with a thesis on the mobile agent programming paradigm. He obtained a Ph.D. in Computer Engineering in 2001.
From May 2001 to May 2008 he was a researcher assistant first at the Department of Computer and Telecommunication Engineering and then at the Department of Mathematics and Informatics, at the University of Catania. In June 2008 he got a permanent position of Researcher at the Department of Mathematics and Informatics (DMI) of the University of Catania. At the DMI, he heads the ARSLAB, an educational and research laboratory on autonomous and robotic systems. The laboratory consists of 3 faculty members, 2 Ph.D. students and 1 post-doc. The research interests include mobile agents; middlewares and platforms for multi-agent systems; programming languages
and approaches for autonomous agents, robots and multi-agent system; complex systems; algorithms for emerging behaviours.



Department of Mathematics and Informatics – University of Catania
Viale Santa Sofia 64


[OptiMAS] Optimisation in Multi­agent Systems.

Jesús Cerquides Bueno and Juan A. Rodríguez­Aguilar.

Abstract. Research on optimisation in multi­agent systems (MASs) has contributed with a wealth of
techniques to solve many of the challenges arising in a wide range of multi­agent application domains. Multi­agent optimization focuses on casting MAS problems into optimization  problems. The solving of those problems could possibly involve the active participation of the  agents in a MAS. Research on multi­agent optimization has rapidly become a very technical, specialized field. Moreover, the contributions to the field in the literature are largely
scattered. These two factors dramatically hinder access to a basic, general view of the  foundations of the field. This tutorial is intended to ease such access by providing a gentle  introduction to fundamental concepts and techniques on multi­agent optimization. The  tutorial will combine theory and practice. Thus, half of the tutorial will consist in a hands­on  session during which students will be able to learn how to solve different multi­agent  optimisation problems using state­of­the art optimisation libraries.

Short CV.

Jesus Cerquides is a tenured scientist at the Artificial Intelligence Research Institute (IIIA) of the Spanish Council for Scientific Research where he works in the Department of Learning Systems. Formerly he was associate professor in the University of Barcelona. Prior to that he was head of iSOCOLab, Chief Technology Officer, co­founder and member of the board at Intelligent Software Components and Associate Director at UBS IT Research. His
research concentrates in the areas of machine learning, distributed optimization and multi­agent systems.

Juan A. Rodriguez­ Aguilar received a Ph.D. degree in Computer Science from the Autonomous University of Barcelona in 2001. He is a tenured scientist at the Artificial Intelligence Research Institute of the Spanish Research Council, where he heads the Department of Multi­agent Systems. His research interests encompass multi­agent
optimization, auctions and economic models, industrial applications of artificial intelligence, and artificial social systems. He is an ECCAI fellow.


Jesús Cerquides Bueno
Artificial Intelligence Research Council
Spanish Council for Scientific Research
e­mail: cerquide​@iiia.csic.es

Juan A. Rodríguez­Aguilar
Artificial Intelligence Research Council
Spanish Council for Scientific Research
e­mail: ​jar@iiia.csic.es