Archived Webinars

 

Algorithms that Play and Design Games

 Screen Shot of video of webinar

 

From the IEEE Computational Intelligence Society (CIS), this webinar by Julian Togelius discusses how methods from the computational intelligence toolbox--including evolutionary computation, neural networks, and Monte Carlo Tree Search--can be adapted to address the research challenges in developing algorithms that can play or design a wide variety of games as well as humans, or even better.

The race is on to develop algorithms that can play a wide variety of games as well as humans, or even better. We do this both to understand how well our algorithms can solve tasks that are designed specifically to be hard for humans to solve, and to find software that can help with game development and design through automatic testing and adaptation. After recent successes with Poker and Go, the attention is now shifting to video games such as DOOM, DoTA, and StarCraft, which provide a fresh set of challenges. Even more challenging is designing agents that can play not just a single game, but any game you give it. A different kind of challenge is that of designing algorithms that can design games, on their own or together with human designers, rather than play them. I will present several examples of how methods from the computational intelligence toolbox, including evolutionary computation, neural networks, and Monte Carlo Tree Search, can be adapted to address these formidable research challenges.

Biography: Julian Togelius is an Associate Professor in the Department of Computer Science and Engineering, New York University, USA. He is also a co-founder of the game AI company modl.ai. Julian works on artificial intelligence for games and games for artificial intelligence. His current main research directions involve search-based procedural content generation in games, general video game playing, player modelling, generating games based on open data, and fair and relevant benchmarking of AI through game-based competitions. He is the Editor-in-Chief of IEEE Transactions on Games and has been chair or program chair of several of the main conferences on AI and Games. Togelius holds a BA from Lund University, an MSc from the University of Sussex, and a PhD from the University of Essex.

 

Past Webinars: 2019

 

Joao Carvalho35 Webinar Speaker: Prof. Joao Paulo Carvalho
Webinar Chair: Dr Keeley Crockett
Title: Recommender Systems: Using Fuzzy Fingerprints for "Proper" Recommendations
Date & Time: 26th November at 16:00 (GMT) Find your local time
Registration URL: https://attendee.gotowebinar.com/register/4709075663532294668

Abstract
Most Recommender Systems rely exclusively on ratings and are known as Memory-based Collaborative Filtering systems. This is the currently dominant approach outside of academia due to the low implementation effort and service maintenance, when compared with more complex Model-based approaches. Traditional Memory-based systems have as their main goal to predict ratings, using similarity metrics to determine similarities between the users’ (or items) rating patterns. In this talk, we propose item and user-based Fuzzy Collaborative Filtering approaches that do not necessarily rely on rating prediction, instead leveraging on Fuzzy Fingerprints to create a novel similarity based recommendation approach. Fuzzy Fingerprints provide a concise and compact representation of users allowing the reduction of the dimensionality usually associated with user-based collaborative filtering.

Biography:
Prof. Joao Paulo Carvalho has a PhD (2002), a MSc (1996) and an Electrical and Computer Engineer (1992) degree from Instituto Superior Técnico, University of Lisbon, Portugal, where he is currently a Tenured Associate Professor at the Department of Electrical Engineering and Computers. He has lectured courses on Computational Intelligence, Distributed Systems, Entrepreneurship and Technology Transfer, Computer Architectures and Digital Circuits since 1999. He is in the Board of Directors of INESC-ID, where he is a senior researcher and has coordinated 5 funded research projects and participated in more than a dozen national and European funded projects.
His current main research interest involves developing and applying new Computational Intelligence techniques to natural language processing, text mining, social network analysis, social sciences and earth sciences. He has authored over 130 papers in international scientific Journals, books and peer-reviewed conferences. He is Area Editor of Fuzzy Sets and Systems and was Associate Editor of 2 other international Journals. He will be the General Chair of IPMU2020, was program co-chair and organizer of IFSA-EUSFLAT2009, Web chair of the 2010 IEEE World Congress on Computational Computation, FUZZ-IEEE2015 and FUZZ-IEEE2017 Publicity-chair, IPMU2016 program-chair, IEEE-WCCI2017 PR and Publicity-chair, and is PC member of more than 30 international conferences. ​


Simone Ludwig Webinar Speaker: Prof. Simone Ludwig
Webinar Chair: Dr Keeley Crockett
Title: Classification in Action: Neural Networks and Differential Evolution Classifier applied to Intrusion Detection and Cancer Data
Date & Time: 11th November at 10 AM (CDT), 3pm in the UK (GMT) Find your local time
Webinar ID: 358-222-843

Abstract:
This presentation will talk about two research projects. The first investigates an Intrusion Detection data set applying a neural network ensemble classifier, and the second looks at different cancer data sets applying a differential evolution classifier. The presentation will start with an introduction to neural networks and will then describe the deep neural network ensemble that was applied to Intrusion detection data. The classification task is to differentiate between normal and intrusive behavior in a network. The second project proposes a cost-sensitive version of the centroid-based classification algorithm using differential evolution. Four imbalanced cancer data sets (Breast, Lung, Uterus, and Stomach) are investigated. The experiments investigate the survivability of cancer patients compared to the performance of the current variants. Moreover, the performance of the proposed version is compared with the performance of five cost-sensitive machine-learning algorithms.

Biography:
Dr. Simone Ludwig is a Professor of Computer Science at North Dakota State University (NDSU, US) since 2010. Prior to joining NDSU she worked at the University of Saskatchewan (Canada), Concordia University (Canada), Cardiff University (UK) and Brunel University (UK). Dr. Ludwig received her PhD degree and MSc degree with distinction from Brunel University (UK), in 2004 and 2000, respectively. Before starting her academic career she worked several years in the software industry. Her research interests lie in the area of computational intelligence including swarm intelligence, evolutionary computation, neural networks, and fuzzy reasoning. Examples of application areas where computational intelligence methods are applied to are data mining (including big data), image processing, intrusion detection, cryptography, and cloud computing.​​


884helbigmardimgDynamic Multi-Objective Optimization: Challenges and Opportunities

Webinar Speaker: Dr Mardé Helbig

Date and Time: October 3, 2019, 1 pm (GMT) Time Zone Converter

Abstract: Most optimization problems in real-life have more than one objective, with at least two objectives in conflict with one another and at least one objective that changes over time. These kinds of optimization problems are referred to as dynamic multi-objective optimization (DMOO) problems.

Most research in multi-objective optimization has been conducted on static problems and most research on dynamic problems has been conducted on single-objective optimization. The goal of a DMOO algorithm (DMOA) is to find an optimal set of solutions that is: as close as possible to the true set of solutions and a diverse set of solutions. However, in addition to these goals, a DMOA has to track the changing set of optimal solutions over time.

This talk will introduce the participants to the field of DMOO, challenges in the field that are not yet addressed, such as incorporating a decision maker’s preference in DMOO and visualizing the behaviour of DMOAs; real-world applications; and emerging research fields that provide interesting research opportunities.

Biography: Mardé Helbig is a Senior Lecturer at the University of Pretoria, South Africa. She obtained her PhD in 2012 at the University of Pretoria. She has been the main organizer of special sessions on DMOO at numerous conference and competition on DMOO at CEC 2015. She also presented a tutorial on DMOO at SSCI 2015, the International Conference on Swarm Intelligence 2016 and WCCI2018. She was invited as a keynote speaker on DMOO at the International Conference on Soft Computing and Machine Learning 2016, the International Conference on Mechanical and Intelligent Manufacturing Technologies 2017 to 2019, and presented the first Memorial Lecture of Prof Zadeh at ISCMI 2017. She is a regular reviewer for the top conferences and journals in the field. In addition, she is the chair of the IEEE CIS South Africa, a sub-committee member of the IEEE CIS WCI.


 

james yuWebinar SpeakerDr James Yu
Title: Deep learning on graphs with applications in smart cities research
Date & Time: Wed, Jul 3, 2019 1:00 PM - 2:00 PM GMT

Description:

Deep learning is successful in many research and engineering domains, ranging from acoustics, images to natural language processing. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges to apply deep learning to the ubiquitous data structure. Recently, a significant amount of research efforts have been devoted to this area, greatly advancing graph analyzing techniques. In this talk, I will provide an introductory overview of graph neural networks in data mining and machine learning fields, with a focus on graph convolutional networks. I will review alternative architectures that have recently been developed, and discuss the applications of graph neural networks on classical network-related tasks and recent applications in smart cities research.

Biography:

James is an assistant professor at Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), China, and an honorary assistant professor at Department of Electrical and Electronic Engineering, the University of Hong Kong. He is also the chief research consultant of GWGrid Inc. (Zhuhai) and Fano Labs (Hong Kong). He received the B.Eng. and Ph.D. degree from the University of Hong Kong in 2011 and 2015, respectively. Before joining SUSTech, he was a post-doctoral fellow at the University of Hong Kong. He is broadly interested in smart city and urban computing, deep learning, intelligent transportation systems, and smart energy systems. He is an Editor of the IET Smart Cities journal and the Leading Editor of its special issue on Smart Transport.


 

Vesna Webinar SpeakerDr Vesna Šešum-Čavić
Title: Bio-Inspired Intelligence in Coping with Complexity of Distributed Software Systems
Date & Time: Thu, Jun 6, 2019 6:00 AM - 7:00 AM EDT

Description:

CI plays an important role in designing self-organizing software for distributed systems, which are typically characterized by a huge problem size concerning number of computing devices, clients, requests and size of queries, autonomy and heterogeneity of participating organizations, and dynamic changes of the environment. In such settings, the common approach of one central coordinator reaches its technical and conceptual limits - it represents a single point of failure with the risk of becoming a performance and availability bottleneck. To cope with unforeseen dynamics, autonomously acting components whose behaviours implement bio-inspired algorithms are a promising approach. These types of algorithms are particularly important and inevitable for the optimization and robustness of highly dynamic distributed systems. Depending on the problem area, application of bio-inspired algorithms enables different kinds of self-organization. This presentation focuses on swarm-inspired algorithms and their remarkable power that lies in coordination of all individuals and communication of “knowledge” without supervision, Swarm-inspired intelligence could help highly dynamic systems to cope with environmental changes. Specific use-cases discussed are load balancing in heterogeneous distributed systems and information retrieval in the Internet as well as how swarm intelligence can be mapped/adapted to these problems.

Biography: Vesna Šešum-Čavić is a senior scientist and university lecturer in computational intelligence, Institute of Information Systems Engineering, Compilers and Languages Group, Vienna University of Technology, Austria. Her research interests cover swarm intelligence, network optimization, p2p systems, theory and design of algorithms, combinatorial optimization, complex systems, self-organization, multi-agent systems. She was a conference chair/program committee member of international conferences. Vesna is the current Chair of Women in Computational Intelligence.

 


 

 

Annabel LathamWebinar Speaker: Dr. Annabel Latham

Title: Automated Profiling of Individual Traits: Modelling Learning Styles with Oscar Conversational Intelligent Tutoring System

Date & Time:
Mon, Apr 8, 2019: 3:00 PM - 4:00 PM GMT
Find your local time

Description:

Abstract-Use of computational intelligence methods for automated user profiling have been widely publicised recently following the Cambridge Analytica / FaceBook scandal, with implications for the ethics and governance of tracking data held on social media and other platforms. Debate over the validity of psychological models of personality and learning styles is not new, however adaptive and targeted online advertisements that rely on such models are big business. This webinar explores the application of intelligent systems to user profiling in the online learning domain. It will describe research to develop methods for profiling individual traits in a learning context, introducing a Conversational Intelligent Tutoring System called Oscar and the experiments to automatically predict each individual learner’s preferred style in order to provide an adaptive learning experience. Using the Felder-Silverman model of learning styles, a set of typical behaviours is mapped to a set of variables to capture each learner’s behaviour during an adaptive conversational tutorial. The complexities of capturing data implicitly during a real-time tutoring conversation in a live teaching/learning environment will be discussed. A number of methods and algorithms (e.g. rule-based, MLP neural networks, decision trees, fuzzy decision trees) were applied to the behaviour dataset to determine the best predictions for each of the 4 dimensions of learning style, and for attribute selection to reduce time/complexity for application in real-time tutoring conversations. 

Biography:

Annabel M. Latham received a BSc(Hons) degree in Computation from UMIST and (following several years in the software industry) achieved MSc and PhD degrees in Computer Science from Manchester Metropolitan University, UK. Annabel is a Senior Lecturer in School of Computing & Mathematics at Manchester Met. Her research interests include conversational agents, intelligent tutoring systems, affective computing, user profiling and computational intelligence. She is a Fellow of the Higher Education Academy and a Senior Member of the IEEE, IEEE Computational Intelligence Society and IEEE Women in Engineering Society. She is currently Chair of IEEE UKI Women in Engineering, Chair of IEEE CIS Graduate Student Research Grants subcommittee and a committee member of the IEEE CIS Education committee and IEEE CIS Women in Computational Intelligence subcommittee (IEEE CI society).

Archved Webinar: https://www.gotostage.com/channel/22f0d21ba18c4200ba79f732230520af/recording/8bde288eaf1c4c0baec08aedead7f4d2/watch


 

alice smithWebinar Speaker: Professor Alice Smith

Title: Evolutionary Strategies for Difficult Engineering Design Problems

Date & Time:
Mon, Apr 1, 2019: 4:00 pm – 5:00 pm BST (London)
Find your local time

Description:

Abstract- This presentation will put forth several straightforward but successful implementations of an often overlooked evolutionary algorithm – evolutionary strategies, ES – for the design of complex systems. ES was developed more than 50 years ago for optimizing engineering design problems in continuous space and is characterized by its simplicity and computational efficiency. There are few tunable parameters in the basic version and the search relies on the evolution of a population through mutation only, where mutation is a Gaussian which adapts automatically to the search history. Such simplicity is appealing for both algorithm development and implementation and tends to result in a robust search. The engineering design problems showcased in this talk are diverse and most involve two objectives optimized with ES simultaneously to identify a Pareto set of non-dominated designs. The applications are (1) the design of an airfoil for a flying drone considering drag and lift, (2) the design of heterogeneous communications networks considering resiliency and traffic efficiency, (3) the location of semi-obnoxious facilities in municipalities considering transport costs and social costs, and (4) the design of large order picking warehouses considering travel distance. 

Biography:

ALICE E. SMITH is the Joe W. Forehand/Accenture Distinguished Professor of the Industrial and Systems Engineering Department at Auburn University. Dr. Smith’s research focus is analysis, modelling and optimization of complex systems with emphasis on computation inspired by natural systems. She holds one U.S. patent and several international patents and has authored more than 200 publications. Dr. Smith has been a principal investigator on over $8.5 million of sponsored research with funding including NASA, U.S. Department of Defense, Missile Defense Agency, National Security Agency, NIST, U.S. Department of Transportation, and Lockheed Martin. She is a FIEEE and FIISE. 

 


Past Webinars: 2018

Title:  Developments in Type-2 Fuzzy Logic

Speaker:   Professor Jon Garibaldi

Chair: Keeley Crockett

Date and Time: 10th December 2018. 16.00 (GMT)

Abstract:

Type-2 fuzzy sets and systems, including both interval and general type-2 sets, are now firmly established as tools for the fuzzy researcher that may be deployed on a wide range of applications and in a wide set of contexts. However, in many situations the output of type-2 systems are type-reduced and then defuzzified to an interval centroid, which are then often even simply averaged to obtain a single crisp output. Many successful applications of type-2 have been in control contexts, often focussing on reducing the RMSE. This is not taking full advantage of the extra modelling capabilities inherent in type-2 fuzzy sets. In this talk, I will present some recent research being carried out within the LUCID group at Nottingham into type-2 for modelling human reasoning. I will cover approaches and methodologies which make more use of type-2 capabilities, illustrating these with reference to practical applications such as classification of breast cancer tumours, modelling expert variability, and other decision support problems.

Webinar ID: 879-280-491

Speaker Bio:

Professor Jon Garibaldi received the BSc degree in Physics from University of Bristol, UK, in 1984, and MSc degree and PhD degree from the University of Plymouth, UK, in 1990 and 1997, respectively. Prof. Garibaldi is currently Head of School of Computer Science, University of Nottingham, Head of the Intelligent Modelling and Analysis (IMA) Research Group, Member of the Lab for Uncertainty in Data and Decision Making (LUCID) and joint Director of the Advanced Data Analysis Centre (ADAC). His main research interests include modelling uncertainty and variation in human reasoning, and in modelling and interpreting complex data to enable better decision making, particularly in medical domains. Prof. Garibaldi is the current Editor-in-Chief of IEEE Transactions on Fuzzy Systems. He has served regularly in the organising committees and programme committees of a range of leading international conferences and workshops, such as FUZZ-IEEE, WCCI, EURO and PPSN.

 

Title: Knowledge discovery with Genetic Programming based Symbolic Regression

Speaker: Professor Qi Chen

Chair: Bing Xue

Date and Time: 11th June 2018 at 09:00 BST. This is 8am GMT time (due to British summer time)

Abstract:
Genetic Programming (GP) based symbolic regression, as a kind of regression analysis, is to find the relationship between the input data and the output data and express this relationship in a mathematical model for some given data of the unknown process. GP based symbolic regression provides a way to getting a good insight into the data generating systems. It is extremely useful when we do not have any domain knowledge of the data generating process. At the same time, by not requiring any specific model and letting the patterns in the data itself reveal the appropriate models, GP based symbolic regression is not affected by human bias. It is clear that the importance of GP based symbolic regression will increase as the complexity of the solved problems are increasing in science and industry. In this webinar, we will discuss the background and basic mechanism of GP based symbolic regression, and enhancements that have improved symbolic regression.

Webinar ID: 208-996-579

Speaker Bio:
Qi Chen received the B.E. degree in automation from the University of South China, Hunan, China, in 2005, the M.E. degree in software engineering from the Beijing Institute of Technology, Beijing, China, in 200, and the Ph.D. degree in Computer Science from Victoria University of Wellington (VUW), Wellington, New Zealand. Since 2014, she has been with the Evolutionary Computation Research Group, VUW. Her current research interests include genetic programming for symbolic regression, machine learning, evolutionary computation, feature selection, feature construction, transfer learning, domain adaptation, and statistical learning theory. Ms Chen serves as a Reviewer of international conferences, including the IEEE Congress on Evolutionary Computation, and international journals, including the IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, Knowledge-based Systems and the Journal of Heuristics.

 

Title: Challenging the stigma surrounding the role of women in technology, a journey from combinatorial optimization to IBM

Speaker: Dr Amy Khalfay, Early Career Researcher, IBM

Date and Time: 8th June 2018 at: 15:00 BST

Abstract:
This session will cover the role of females within the technology and wider STEM sector. Many people feel that you must have studied a certain degree, know a programming language, or prefer to work alone to be able to have a career in technology. This is not the case, these careers are open to everyone, from any background. During this session we will be exploring some of the misconceptions about careers within STEM, discovering the many types of roles and doing some myth busting. We will also discuss my personal journey to becoming a graduate technology consultant for IBM, my background of research and my commitment to ensure more females enter STEM careers. My PhD, titled "Optimization heuristics for solving technician and task scheduling problems", focused on solving NP hard combinatorial optimization problems that arise in the real world and was sponsored by industry. The project enabled me to enhance my soft skills, write academically, learn to code and develop a deeper understanding of real world business problems and innovative ways to solve them.

Webinar ID: 938-415-115

Speaker Bio:
Dr Amy Khalfay is currently a graduate technology consultant for IBM, joining in October 2017. Prior to this Amy completed a BSc in Mathematics (2014) and a PhD in Operational Research (2017). Amy is also a committee member of IEEE Women in Engineering. Amy’s research area is combinatorial optimisation, solving NP hard scheduling problems. Areas of skill include Java, Statistical Analysis, Mathematical Modelling and Algorithm Design and Development.

 

Title: The Social and Ethical Implications Of Computational Intelligence

Speaker: Dr Matt Garratt, University of New South Wales

Date and Time: 17th May 2018 at 09:00 BST.

Abstract:
Computational Intelligence (CI) is a term encompassing a basket of soft-computing methodologies used to solve problems that are not suited to solution by mathematical or other traditional methods. CI techniques include technologies such as fuzzy logic, artificial neural networks, deep learning, evolutionary computation and cognitive and developmental systems. Today, CI techniques are embodied within many technologies ranging from autonomous driverless cars to automated decision making on the stock exchange. These technologies already have a significant positive effect on the global economy and when used properly can greatly enhance the lives of many people. There are however risks with misuse of CI. In this webinar, we will discuss what the moral principles should be that govern the behaviour of CI technology, as well as the designer. These principles cover balancing the ecological footprint of technologies against the economic benefits, managing the impact of automation on the workforce, ensuring privacy is not adversely affected and dealing with the legal implications of embodying CI technologies in autonomous systems.

Speaker Bio:
Matthew A Garratt received a BE degree in Aeronautical Engineering from Sydney University, Australia, a graduate diploma in applied computer science from Central Queensland University and a PhD in the field of biologically inspired robotics from the Australian National University in 2008. Prior to entering academia, Matt worked as an engineer in the Royal Australian Navy for a decade. Some of his research successes include demonstration of terrain following using vision for an unmanned helicopter, landing an unmanned helicopter onto a moving deck simulator and control of helicopters using neural networks. Some of his current research projects include achieving autonomous flight in cluttered environments using monocular cameras and range sensors, landing UAVs on moving platforms, adaptive flight control for flapping wing and rotary wing vehicles, trusted human-autonomy teaming in teleoperations and self-organising Unmanned Systems in contested RF environments. He is an Associate professor with the School of Engineering and Information Technology (SEIT) at the University of New South Wales, Canberra. Matt is currently the Deputy Head of School (Research) in SEIT and is the chair of the Computational Intelligence Society task force on the Ethics and Social Implications of CI. His research interests include sensing, guidance and control for autonomous systems with particular emphasis on biologically inspired and Computational Intelligence approaches.

 

Title: Type-2 Fuzzy Logic Systems and Their Applications

Speaker: Prof. Hani Hagras

Date and Time: 6pm (BST), April 30th, 2018

Abstract:
This topic will present a brief overview on theoretical and practical coverage of the area of type-2 fuzzy logic systems and their applications. The talks will cover the following topics

  • Introduction to Type-2 Fuzzy Logic Sets and systems and their theoretical basis
  • Practical Implementation of Interval Type-2 Fuzz Logic Systems and their various applications
  • Emerging areas of type-2 fuzzy logic systems

 

Biography:
Prof. Hani Hagras is a Professor of Computational Intelligence, Director of Research. Director of the Computational Intelligence Centre, Head of the Fuzzy Systems Research Group and Head of the Intelligent Environments Research Group in the University of Essex, UK. He is a Fellow of Institute of Electrical and Electronics Engineers (IEEE) and he is also a Fellow of the Institution of Engineering and Technology (IET). and Principal Fellow of the UK Higher Education Academy. His major research interests are in computational intelligence, notably type-2 fuzzy systems, fuzzy logic, neural networks, genetic algorithms, and evolutionary computation. His research interests also include ambient intelligence, pervasive computing and intelligent buildings. He is also interested in embedded agents, robotics and intelligent control. He has authored more than 300 papers in international journals, conferences and books. His work has received funding that totalled to about £5 Million in the last five years from the European Union, the UK Technology Strategy Board (TSB), the UK Department of Trade and Industry (DTI), the UK Engineering and Physical Sciences Research Council (EPSRC), the UK Economic and Social Sciences Research Council (ESRC) as well as several industrial companies including. He has also Five industrial patents in the field of computational intelligence and intelligent control. His research has won numerous prestigious international awards where most recently he was awarded by the IEEE Computational Intelligence Society (CIS), the 2013 Outstanding Paper Award in the IEEE Transactions on Fuzzy Systems and also he has won the 2004 Outstanding Paper Award in the IEEE Transactions on Fuzzy Systems. He was also awarded the 2015 Global Telecommunications Business award for his joint project with British Telecom. In 2016, he was elected as Distinguished Lecturer by the IEEE Computational Intelligence Society. He was also the Chair of the IEEE CIS Chapter that won the 2011 IEEE CIS Outstanding Chapter award. His work with IP4 Ltd has won the 2009 Lord Stafford Award for Achievement in Innovation for East of England. His work has also won the 2011 Best Knowledge Transfer Partnership Project for London and the Eastern Region. His work has also won best paper awards in several conferences including the 2014 and 2006 IEEE International Conference on Fuzzy Systems and the 2012 UK Workshop on Computational Intelligence. He served as the Chair of IEEE Computational Intelligence Society (CIS) Senior Members Sub-Committee. He served also as the chair of the IEEE CIS Task Force on Intelligent Agents. He is currently the Chair of the IEEE CIS Task Force on Extensions to Type-1 Fuzzy Sets. He is also a Vice Chair of the IEEE CIS Technical Committee on Emergent Technologies. He is a member of the IEEE Computational Intelligence Society (CIS) Fuzzy Systems Technical Committee. He served also as a member of the IEEE CIS Fellows Committee. He serves also as a member of the IEEE CIS conferences committee. He is an Associate Editor of the IEEE Transactions on Fuzzy Systems. He is also an Associate Editor of the International Journal of Robotics and Automation. Prof. Hagras chaired several international conferences where he will act as the Programme Chair of the 2017 IEEE International Conference on Fuzzy Systems.


Webinars: 2017

Title: The Magic of Monte Carlo Tree Search

Speaker : Dr. Mark Winands, Associate Professor, Department of Data Science and Knowledge Engineering, Maastricht University.

Date and Time: 4pm (BST), Friday, Sept. 29, 2017

Abstract
Monte-Carlo Tree Search (MCTS) has caused a revolution in computer game-playing the last few years. The most well-known example is the game of Go. MCTS is a best-first search technique that gradually builds up a search tree, guided by Monte-Carlo simulations. In contrast to many classic search techniques, MCTS does not require a heuristic evaluation function that assesses the current board position. In this talk I will discuss its background, basic mechanism, and standard enhancements that have improved the technique considerably. Successful applications of the technique in several domains will be mentioned. I will particular elaborate on MCTS agents in real-time games. These games are characterized by uncertainty, a large state space, and open-endedness. However, MCTS copes well when limited time is available between moves as it can be terminated anytime to select a move. In this talk I will discuss our applications of MCTS for Ms Pacman, StarCraft, and the General Video Game AI framework.

Bio
Mark Winands received a Ph.D. degree in Artificial Intelligence from the Department of Computer Science, Maastricht University, Maastricht, The Netherlands, in 2004. Currently, he is an Associate Professor at the Department of Data Science and Knowledge Engineering, Maastricht University. His research interests include heuristic search, machine learning and games. He has written more than eighty scientific publications on Games and AI. Mark serves as an editor-in-chief of the ICGA Journal, associate editor of IEEE Transactions on Computational Intelligence and AI in Games, editor of Game and Puzzle Design. He is a member of the Games Technical Committee (GTC) | IEEE Computational Intelligence Society, and member of working group 14.4 – Entertainment Games | IFIP TC14 on Entertainment Computing.

 

Title: Bridge: a new challenge for AI?

Speaker: Dr. Véronique Ventos, Associate Professor at University Paris-Saclay (France)

Date and Time: 5pm (CET), Monday, Nov. 20, 2017

 

Abstract
Games have always been an excellent field of experimentation for the nascent techniques in computer science and in different areas of Artificial Intelligence (AI) including Machine Learning (ML). Despite their complexity, game problems are much easier to understand and to model than real life problems. Systems initially designed for games are then used in the context of real applications. In the last decades, designs of champion-level systems dedicated to a game (game AI) were considered as milestones of computer science and AI. Go and Poker are the two most recent successes. In May 2017, AlphaGo (DeepMind) defeated by 3 to 0 the Go world champion Ke Jie. In January 2017, the Poker AI Libratus (Carnegie Mellon University) won a heads-up no-limit Texas hold'em poker event against four of the best professional players. This success has not yet happened with regard to another incomplete information cards game, namely Bridge, which then provides a challenging problem for AI. We think that Deep Learning (DL) cannot be the only AI future. There are many Machine Learning and more generally AI fields which can interact with DL. Bridge is a great example of an application needing more than black box approaches. The AlphaBridge project is dedicated to the design of a Bridge AI taking up this challenge by using hybrid framework in the field of Artificial Intelligence. The first part of the webinar is devoted to the presentation of the different aspects of bridge and of various challenges inherent to it. In a second part, we will present our work concerning the optimization of the AI Wbridge5 developed by Yves Costel. This work is based on a recent seed methodology (T. Cazenave, J. Liu and O. Teytaud 2015, 2016) which optimizes the quality of Monte-Carlo simulations and which has been defined and validated in other games. The Wbridge5 version boosted with this method won the World Computer-Bridge Championship twice, in September 2016 and in August 2017. Finally, the last part is about various ongoing works related to the design of a hybrid architecture entirely dedicated to bridge using recent numeric and symbolic Machine Learning modules.

Bio
PhD in Artificial Intelligence (Knowledge Representation and Machine Learning) in 1997. Associate professor at University Paris Saclay, France since 1998. Before joining in 2015 the group A&O in the interplay of Machine Learning and Optimization, she worked in the group LaHDAK (Large-scale Heterogeneous DAta and Knowledge) at Laboratory of Computer Science (LRI). She started playing bridge in 2004 and is now 59th French woman player out of 48644 players. In 2015, she set up the AlphaBridge project combining her two passions. AlphaBridge is dedicated to solve the game of bridge by defining a hybrid architecture including recent numeric and symbolic Machine Learning modules.

 

 

Prof. Simon M. Lucas has agreed to give the Webinar on "Game AI and Noisy Optimisation" on Sep 11, 2017 5:00 PM BST.

The details of the Webinar and the Bio for the presenter have been presented below:

Bio: Simon M. Lucas is a professor of Artificial Intelligence and head of the School of Electronic Engineering and Computer Science at Queen Mary University of London. He holds a PhD degree (1991) in Electronics and Computer Science from the University of Southampton. His main research interests are games, evolutionary computation, and machine learning, and he has published widely in these fields with over 200 peer-reviewed papers. He is the inventor of the scanning n-tuple classifier, and is the founding Editor-in-Chief of the IEEE Transactions on Computational Intelligence and AI in Games.

Many problems in game AI can be viewed as noisy optimisation problems, where the noise or uncertainty can stem from many sources, including: games which are naturally stochastic, agents which follow stochastic policies (such us Monte Carlo Tree Search or Rolling Horizon Evolution), and noise when evaluating the quality of games. The problems arise when trying to optimise agents to play games well, or just in a particular way, when trying to optimise heuristics for Monte Carlo Tree Search, and when trying to optimise games in order to provide a particular style of player experience (such as a game where the player has to react quickly, or has to plan strategically). The webinar will show examples of how these problems arise, and describe recent research in efficient noisy optimisation that uses novel model-based optimisation algorithms to provide efficient and effective search.

After registering, you will receive a confirmation email containing information about joining the webinar.

 


Webinars: 2016

 

Speaker: Dr. Yi Mei
Title: Genetic Programming Hyper-heuristics for Combinatorial Optimisation
Date & Time: Thu, Dec 15, 2016 9:30 AM - 10:30 AM NZDT (Wellington Time)
Series: IEEE CIS Standard Webinars
Location: Online Webinar
Slides and other resources: http://homepages.ecs.vuw.ac.nz/~yimei/IEEEWebinar.pptx
Video: https://ieeetv.ieee.org/ieeetv-specials/genetic-programming-hyper-heuristics-for-combinatorial-optimisation-yi-mei-cis-webinar

Speaker: Swati Aggarwal *** 1st place in the competition
Title: Computational approach to tame chaotic data in Big Data
Date & Time: 11th October 2016
Series: 2016 IEEE CIS Webinars Competition
Location: YouTube video https://youtu.be/mCP3Fz6cCE8

Speaker: Rodrigo G. F. Soares *** 2nd place in the competition
Title: Semi-supervised Classification with Cluster Regularisation
Date & Time: 15th August 2016
Series: 2016 IEEE CIS Webinars Competition
Location: YouTube video https://youtu.be/3xeqtXNp20k

Speaker: Mathialakan Thavappiragasam
Title: Low Cost Bitwise Manipulation of Mass Logical Data
Date & Time: 11th August 2016
Series: 2016 IEEE CIS Webinars Competition
Location: YouTube video https://youtu.be/7CMOGdyvfCk

Speakers: Renato Assuncao
Title: A Bayesian Approach for Spatial Clustering
Date & Time: 14th November 2016, 17:00 Brasilia (BRT) time
Series: Standard Webinar Series
Location: Online Webinar
Video: https://ieeetv.ieee.org/ieeetv-specials/a-bayesian-approach-for-spatial-clustering-ieee-cis-webinar?rf=channels|56&

Speakers: Yang Yu
Title: Towards Evolutionary Approximate Optimization for Machine Learning
Date & Time: 31st October 2016, 21:00 Beijing time
Series: Standard Webinar Series
Location: Online Webinar
Slides and other resources: http://lamda.nju.edu.cn/yuy/ciswebminar.ashx

Speakers: Andre C. P. L. F. de Carvalho
Title: Algorithm recommendation using metalearning
Date & Time: 30th September 2016, 15:00 Brasilia Time (BRT)
Series: Standard Webinar Series
Location: Online Webinar
Video: https://ieeetv.ieee.org/ieeetv-specials/algorithm-recommendation-using-metalearning?rf=channels|56&

Speakers: Roberto Furfaro
Title: Intelligent Systems for Deep Space Exploration: Solutions and Challenges
Date & Time: Jul 13, 2016 11:00 AM PT
Series: Standard Webinar Series
Location: Online Webinar
Video: https://ieeetv.ieee.org/ieeetv-specials/intelligent-systems-for-deep-space-exploration-solutions-and-challenges-roberto-furfaro?rf=channels|56

Speakers: Ke Li
Title: Achieving Balance Between Convergence and Diversity in Evolutionary Multi-Objective Optimization
Date & Time: May 25, 2016 17:00 BST
Series: Early Career Researcher
Location: Online Webinar
Video: https://ieeetv.ieee.org/ieeetv-specials/achieving-balance-between-convergence-and-diversity-in-evolutionary-multi-objective-optimization-ke-li?rf=channels|56


Webinars: 2015

 

Speakers: Swati Aggarwal *** 3rd place in the competition
Title: A perspective shift from Fuzzy logic to Neutrosophic Logic
Date & Time: Available at YouTube from 22nd November 2015
Series: 2015 IEEE CIS Webinar Competition Finalists
Video: https://youtu.be/WryVUv5Bq98

Speakers: Joseph Alexander Brown *** 2nd place in the competition
Title: Computational Intelligence Creating Procedural Content for Games
Date & Time: Available at YouTube from 22nd November 2015
Series: 2015 IEEE CIS Webinar Competition Finalists
Video: https://www.youtube.com/watch?v=CeRNlB7RAKQ

Speakers: Osama Salah Eldin *** 1st place in the competition
Title: Mathematical Evolution of Human Behaviors
Date & Time: Available at YouTube from 22nd November 2015
Series: 2015 IEEE CIS Webinar Competition Finalists
Video: https://youtu.be/6nVtFxpDoVA

Speakers: Petr Hurtik
Title: Driver Assistant
Date & Time: Available at YouTube from 21st November 2015
Series: 2015 IEEE CIS Webinar Competition Finalists
Video: https://www.youtube.com/watch?v=CdspdNZo3Qg

Speakers: Alejandro Ramosu
Title: Imprecision management in natural language generation systems through the use of fuzzy sets
Date & Time: Available at YouTube from 22nd November 2015
Series: 2015 IEEE CIS Webinar Competition Finalists
Video: https://youtu.be/5jBZgDj1Blw

Speakers: Autilia Vitiello
Title: E-commerce data integration through nature-inspired algorithms
Date & Time: Available at YouTube from 22nd November 2015
Series: 2015 IEEE CIS Webinar Competition Finalists
Video: https://youtu.be/1WGsh3mFB1M

Speakers: Aimin Zhou, East China Normal University, China
Title: Learning Guided Evolutionary Optimization: An Example on Multi-operator Search
Date & Time: November 5, 2015 2:00-3:00 PM CST (GMT+8)
Series: Standard Webinar Series

Speakers: Per Kristian Lehre, University of Nottingham, UK
Title: When are evolutionary algorithms provably efficient? Runtime analysis of population-based evolutionary algorithms
Date & Time: September 18, 2015 3:00pm UK
Series: Standard Webinar Series
Video: https://www.youtube.com/watch?v=ig58TjWcPlg

Speakers: Pietro Consoli, University of Birmingham, UK
Title: Adaptive Crossover Operator Selection Based on Online Learning and Fitness Landscape Metrics
Date & Time: May 14, 2015 5:00 PM BST
Series: Junior Webinar Series
Video: https://vimeo.com/user9544898/review/128112170/dbef2f7e80


Webinars: 2014

 

Speakers: Ray Zhixi Li, University of Hong Kong, Hong Kong
Title: A GA-based Parameters Optimization of Technical Indicators and Decision Tree for Equity Analysis
Date & Time: November 28, 2014, 12:00 UTC
Location: Online Webinar

Speakers: Alex Jiaqu Yi, University of Hong Kong, Hong Kong
Title: Educational Data Mining: A Strong Tool in New Education Era
Date & Time: November 28, 2014, 11:00, UTC
Location: Online Webinar

Speakers: Andersen Ang Man Shun, University of Hong Kong, Hong Kong
Title: Single Channel EOG-based HCI System For Simple Computer Control
Date & Time: November 7, 2014, 8:00 UTC
Location: Online Webinar

Speakers: Thushan Ganegedara, University of Moratuwa, Sri Lanka
Title: A Cognitive approach towards Multi-Modal Data Learning and Fusion
Date & Time: October 2, 2014, 12:00 GMT
Location: Online Webinar

Speakers: Prof. Eric Verbeek, Eindhoven University of Technology, The Nederlands
Title: The eXtensible Event Stream (XES) standard
Date & Time: June 20, 2014, 15:00 BST (London Time)
Location: Online Webinar


Webinars: 2013

 

Speakers: Prof. Bob John, University of Nottingham, United Kingdom
Title: So you want to be an Academic? Some Tips and Tricks
Date & Time: Wednesday, October 23, 2013, 21:00 Hong Kong
Location: Online Webinar

Speakers: Prof. Jiming Liu, Hong Kong Baptist University, Hong Kong
Title: Big Data and Self-Organized Computing
Date & Time: Wednesday, May 23, 2013, 10:00 Hong Kong
Location: Online Webinar


Webinars: 2012

 

Speakers: Prof. Alfredo Caro, Italy
Title: Decentralized Coordination in Smart Grids by Self Organizing Dynamic Fuzzy Agents
Date & Time: Wednesday, June 27, 2011, 15:00 Italy
Location: Online Webinar
Co-Sponsors: IEEE Italy CIS Chapter, IEEE Toronto Signals & Computational Intelligence Chapter, IEEE Ottawa CIS Chapter

Speakers: Dr. Julio Valdes, National Research Center, in Ontario, Canada
Title: Visual Data Mining with Nonlinear Space Transformations and Virtual Reality
Date & Time: Friday, September 28, 2012, 11:00 EDT (New York time).
Location: Online Webinar


Webinars: 2011

 

Speakers: Prof. Hani Hagras
Title: Type-2 Fuzzy Logic Controllers: A way Forward for Fuzzy Systems in Real World Environments
Date & Time: Tuesday, April 26, 2011, 15:00-16:30 GMT
Location: Online Webinar
Co-Sponsors: IEEE UK and Republic of Ireland CIS Chapter, IEEE Toronto Signals & Computational Intelligence Chapter, IEEE Ottawa CIS Chapter
Website: https://www2.gotomeeting.com/register/792957898


Webinars: 2010

Speakers: Dr. Giovanni Acampora, Ph.D., and Prof. Vincenzo Loia, Ph.D.,
Title: An Open Integrated Environment for Transparent Fuzzy Agents Design: the Fuzzy Markup Language
Date & Time: Wednesday, March 24, 2010, 11:00-12:30 EDT (Toronto time)
Wednesday, March 24, 2010, 16:00-17:30 CET (Salerno time)
Location: Toronto, Canada, and Salerno, Italy
Organizer: IEEE Toronto Signals & Computational Intelligence Joint Chapter
Co-Sponsors: IEEE Italy Section Computational Intelligence Chapter, IEEE Computational Intelligence Society, IEEE CIS Standard Committee, IEEE Region 7 (IEEE Canada)
Website: http://toronto.ieee.ca/events/mar2410a.htm

 

Speaker: Prof. Leonid I. Perlovsky, Ph.D., (recorded) and Prof. Konstantinos N. Plataniotis, Ph.D., P.Eng., FEIC, (live)
Title: Computational Intelligence: Natural Information Processing (Expert Now Module)
Date & Time: Monday, March 8, 2010, 18:30-20:30 EST
Location: Toronto, Canada, IEEE R7
Chapter: Signals & Computational Intelligence Joint Chapter
Website: http://toronto.ieee.ca/events/mar0810.htm

Speaker: Prof. Konstantinos N. Plataniotis, P.Eng., FEIC
Title: Biometrics: Solutions for Security and Authentication (Expert Now module)
Date & Time: Monday, February 15, 2010, 18:30-20:00 EST
Location: Toronto, Canada, IEEE R7
Chapter: Signals & Computational Intelligence Joint Chapter
Website: http://toronto.ieee.ca/events/feb1510.htm


Webinars: 2009

 

Speaker: April Khademi
Title: Automatic Contrast Enhancement Of White Matter Lesions In Flair MRI
Date & Time: Wednesday, November 25, 2009, 18:30-20:00 EST
Location: Toronto, Canada, IEEE R7
Chapter: Signals & Computational Intelligence Joint Chapter
Website: http://toronto.ieee.ca/events/nov2509.htm

Speaker: Kathleen Carley
Title: Dynamic Network Analysis and Security
Date: 8-10 July 2009
Location: Ottawa, Ontario, Canada
Organizer: IEEE Symposium: Computational Intelligence for Security and Defence Applications (CISDA), 2009

Speaker: Simon Haykin
Title: The Cubature Kalman filter
Date: 8-10 July 2009
Location: Ottawa, Ontario, Canada
Organizer: IEEE Symposium: Computational Intelligence for Security and Defence Applications (CISDA), 2009

Speaker: Hussein Abbass
Title: Forward, Reverse and Emerging Dynamics: Can Complex Adaptive Systems Play a Game with the Unknown?
Date: 8-10 July 2009
Location: Camberra, Australia
Organizer: IEEE Symposium: Computational Intelligence for Security and Defence Applications (CISDA), 2009

Speaker: Prof. Simon Haykin
Title: Cognitive Radio: Research Challenges
Date & Time: Friday, April 3, 2009, 15:00-16:00
Location: Toronto, Canada, IEEE R7
Chapter: Signals & Computational Intelligence Joint Chapter
Website: http://toronto.ieee.ca/events/apr0309.htm

Speaker: Dr. Azadeh Kushki
Title: WLAN Positioning: Applications & Theory
Date & Time: Wednesday, November 26, 2008, 16:00-18:00 EST
Location: Toronto, Canada, IEEE R7
Chapter: Signals & Computational Intelligence Joint Chapter
Website: http://toronto.ieee.ca/events/nov2608.htm
Archival URL: http://ieee.ca/diglib/library/kushki/IEEE_Canada_Webinar_Nov26_2008.pdf