Michael North
Vice President, Decision Sciences, Conversant LLC (USA)

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Jiming Liu
Chair Professor of Computer Science, Hong Kong Baptist University

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Petr Skobelev
Smart Solutions / Samara State Airspace and Technical Universities / Institute of Control of Complex Systems of Russian Academy of Science

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Michael

Michael North

Vice President, Decision Sciences, Conversant LLC (USA)

Title: Hammer or Tongs: How Best to Build Agent-Based Models?
Abstract

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.

Biography
Jiming

Jiming Liu

Chair Professor of Computer Science, Hong Kong Baptist University

Title: On Data-Driven Agent-Based Modeling of Complex Systems
Abstract

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.

Biography
Petr Skobelev

Petr Skobelev

Smart Solutions / Samara State Airspace and Technical Universities / Institute of Control of Complex Systems of Russian Academy of Science

Title: Towards Autonomous AI Systems for Resource Management: Applications in Industry and Lessons Learned
Abstract

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.

Biography