PACBB Conference 2018

Agent-based modeling has been widely applied to solve problems in contexts ranging from exploratory research studies to focused industrial practice. The methodologies employed in these efforts to develop and use agent-based models have run the gamut from ad hoc coding to formal methods. This talk will survey the existing methodologies for developing and using agent-based models; consider their relative strengths and weaknesses; and offer insights into how to choose one for your next agent-based modeling project.

He is senior decision sciences team leader focused on producing analytics applications for markets, social systems, and supply chains. His work has ongoing impact by enhancing industry bottom lines, improving government effectiveness, yielding highly cited publications, and producing popular open source software. His open source portfolio can be found at http://drmichaelnorth.github.io/drmichaelnorth/ and his publication history can be found on Google Scholar at https://goo.gl/11UqRs

Complex systems modeling plays a pivotal role in characterizing and understanding biological, ecological, social, and technological systems. This talk will discuss several important issues in developing agent-based complex systems modeling. Specifically, the talk will address the promises and challenges of data-driven agent-based modeling in unveiling hidden mechanisms as well as intrinsic behavior of complex systems. The talk will look into several examples in the fields of computational healthcare and sustainability.

Jiming Liu is Chair Professor of Computer Science and Associate Vice-President (Research) at Hong Kong Baptist University. He received his M.Eng. and Ph.D. degrees from McGill University, having obtained Master of Arts from Concordia University and B.Sc. from East China Normal University. His research interests include: Complex Systems and Autonomy-Oriented Computing; Complex Networks and Web Intelligence; and Computational Healthcare and Sustainability. He is a Fellow of the IEEE, and was the Chair of IEEE Computer Society Technical Committee on Intelligent Informatics. He has served as Editor-in-Chief of Web Intelligence Journal (IOS), and Associate Editor of IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Cybernetics, Big Data and Information Analytics (AIMS), Neuroscience and Biomedical Engineering (Bentham), and Computational Intelligence (Wiley), among others.

Complexity of modern resource management is analyzed and related with a number of decision makers, high variety of individual criteria, preferences and constraints, interdependency of all operations, etc. The overview of existing methods and tools of Enterprise Resource Planning is given and key requirements for resource management are specified. The concept of autonomous Artificial Intelligence (AI) systems for adaptive resource management based on multi-agent technology is discussed. Multi-agent model of virtual market and method for solving conflicts and finding consensus for adaptive resource management are presented. Functionality and architecture of autonomous AI systems for adaptive resource management and the approach for measuring adaptive intelligence and autonomy level in these systems are considered. Results of delivery of autonomous AI solutions for managing trucks and factories, mobile teams, supply chains, aerospace and railways are presented. Considerable increase of enterprise resources efficiency is shown. Lessons learned from industry applications are formulated and future developments of AI for solving extremely complex problems of adaptive resource management are outlined.

Founder, president and product/technology leader of international group of software companies focused on multi-agent solutions for solving complex problems.

Petr studied Computer Science in Samara State Aerospace University in 1997-1983. In 1983 he became engineer and then senior researcher in Physical Institute of Russian Academy of Science. He designed software for real-time control of physical processes in lazers, image processing and pattern recognition, modelling and simulations, expert systems. With the beginning of Perestroyka he organized software company ArtLog for delivering software in Russia, UK, Germany and USA.

In 1991 he met UK Prof. George Rzevski and they started first R&D projects on multi-agent systems. Together they designed intelligent simulation systems for engineers published in England and Germany. In 1997 Petr started Knowledge Genesis (Samara, Russia) for developing multi-agent e-government solutions. In 2000 Magenta Technology (London, UK) was organized for designing industrial multi-agent solutions for logistics, text understanding and data mining. Designed systems were successfully delivered for Tankers International, AON, GIST, Addison Lee, AVIS and other world-known companies.

Now he is working on new generation of distributed multi-agent solutions. The results are 40% increase of efficiency of mobile services for regional gas distributor, 10% increase in efficiency of workshops for National-size factory, high flexibility of scheduling of 3500 cargos for International Space Station, support of real time decision making processes for swarm of satellites, re-schedule high-speed trains in case of unpredictable events, 24% increase of profitability in supply chain for world-known manufacturer of toys, etc.

Petr got PhD degree in Functional Programming Languages in 1986 and Professor Degree in Multi-Agent Systems in 2003. Author of more than 150 publication and 3 patents on multi-agent systems and technologies.

Specialties: Complexity, Multi-Agent Technology, Multi-Agent Solutions for e-Government and Healthcare, Dynamic Schedulers for Factories, Tankers, Taxi, Trucks, Rent-a-Car and Supply Chain Businesses, Semantic Web, Ontologies, Computer Learning, Knowledge Management, Emergent Intelligence and Non-Linear Thermodynamics, Complex Adaptive Systems, Self-Organization and Evolution, Real Time Logistics, Data Mining, Clustering, Text Understanding, Pattern Discovery