T U T O R I A L S

Artificial Intelligence for Cloud Computing Management

Prof. Vincenzo Piuri

University of Milan, Italy, IEEE Fellow (2001), IEEE Society/Council active memberships/services: CIS, ComSoc, CS, CSS, EMBS, IMS, PES, PHOS, RAS, SMCS, SPS, BIOMC, SYSC, WIE


Recent years have seen a growing interest among users in the migration of their applications to the Cloud computing environments. However, due to high complexity, Cloud-based services often experience a large number of failures and security breaches, and consequently, impose numerous challenges on the dependability and resilience of users’ applications.

Unfortunately, current dependability and resilience solutions focus either on the infrastructure itself or on application analysis, but fail to consider the complex inter-dependencies between system components and application tasks.
This aspect is highly crucial especially when Cloud environments are used, as it is increasingly considered nowadays, in critical applications.

Besides, definition of application requirements, allocations of resources to application tasks, and optimization of global management parameters usually are based either on statistical approaches or on heuristics strategies typical of operating research. Computational intelligence may give additional opportunities and flexibility in specifying the requirements especially when they are defined by non-experts and in optimizing the resource allocation and the global management parameters.

This talk will discuss a user-centric, dependability- and resilience-driven framework that considers deploying and protecting users’ applications in the Cloud infrastructure so as to minimize their exposure to the vulnerabilities in the network, as well as offering fault tolerance and resilience as a service to the users who need to deploy their applications in the Cloud.

In this scenario, the talk analyzes the opportunities offered by computational intelligence to specify the characteristics and the requirements of these environments and support their management in the presence of many local optimization minima.

Biography

Professor Vincenzo Piuri has received his Ph.D. in computer engineering at Politecnico di Milano, Italy (1989). He has been Associate Professor at Politecnico di Milano, Italy and Visiting Professor at the University of Texas at Austin and at George Mason University, USA. He is Full Professor in computer engineering at the Università degli Studi di Milano, Italy (since 2000).

His main research interests are: intelligent systems, cloud computing, fault tolerance, signal and image processing, machine learning, pattern analysis and recognition, theory and industrial applications of neural networks, biometrics, intelligent measurement systems, industrial applications, digital processing architectures, embedded systems, and arithmetic architectures. Original results have been published in more than 400 papers in international journals, proceedings of international conferences, books, and book chapters.

He is Fellow of the IEEE, Distinguished Scientist of ACM, and Senior Member of INNS. He has been IEEE Vice President for Technical Activities (2015), IEEE Director, President of the IEEE Computational Intelligence Society, Vice President for Education of the IEEE Biometrics Council, Vice President for Publications of the IEEE Instrumentation and Measurement Society and the IEEE Systems Council, and Vice President for Membership of the IEEE Computational Intelligence Society. He is Editor-in-Chief of the IEEE Systems Journal (2013-19) and Associate Editor of the IEEE Transactions on Computers and the IEEE Transactions on Cloud Computing, and has been Associate Editor of the IEEE Transactions on Neural Networks and the IEEE Transactions on Instrumentation and Measurement.

He received the IEEE Instrumentation and Measurement Society Technical Award (2002) for the contributions to the advancement of theory and practice of computational intelligence in measurement systems and industrial applications. He is Honorary Professor at the Obuda University, Budapest, Hungary (since 2014), Guangdong University of Petrochemical Technology, China (since 2014), the Muroran Institute of Technology, Japan (since 2016), and the Amity University, India (since 2017).

More information are available at http://www.di.unimi.it/piuri

AI/ML for games for AI/ML

Dr Kostas Karpouzis

National Technical University of Athens (NTUA), Greece


Digital games have recently emerged as a very powerful research instrument for a number of reasons: they involve a wide variety of computing disciplines, from databases and networking to hardware and devices, and they are very attractive to users regardless of age or cultural background, making them popular and easy to evaluate with actual players. In the fields of Artificial Intelligence and Machine Learning, games are used in a two-fold manner: to collect information about the players’ individual characteristics (player modelling), expressivity (affective computing) and playing style (adaptivity) and also to develop AI-based player bots to assist and face the human players and as a test-bed for contemporary AI algorithms.

In this tutorial, we will discuss both approaches that relate AI/ML to games: starting from a theoretical review of user/player modelling concepts, we will discuss how we can collect data from the users during gameplay and use them to adapt the player experience or model the players themselves. Following that, we will discuss AI/ML algorithms used to train computer-based players and how these can be used in contexts outside gaming. Finally, we will discuss player modelling in contexts related to serious gaming, such as health and education.

Intended audience: researchers in the fields of Machine Learning and Human-Computer Interaction, game developers and designers, health and education practitioners.

Outline:
– Collecting behavioural and preference data from gameplay
– Player modelling from emerging information
– Player modelling from game behaviour
– Clustering/classification from game behaviour
– Estimating and maximising player experience
– Adapting to player experience
– Machine learning for non-player characters
– Imitating player personalities
– Learning from player behaviour
– Game agents