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My primary research interests lie at the intersection of two fields: evolutionary computation and multi-agent systems. I am specifically interested in how evolutionary search algorithms can be used to help people understand and analyze agent-based models of complex systems (e.g., flocking birds, traffic jams, or how information diffuses across social networks). My secondary research interests broadly span the areas of artificial life, multi-agent robotics, cognitive/learning science, design of multi-agent modeling environments. I enjoy interdisciplinary research, and in pursuit of the aforementioned topics, I have been involved in application areas from archeology to zoology, from linguistics to marketing, and from urban growth patterns to materials science. I am also very interested in creative approaches to computer science and complex systems education, and have published work on the use of multi-agent simulation as a vehicle for introducing students to computer science.
It is my philosophy that theoretical research should be inspired by real-world problems, and conversely, that theoretical results should inform and enhance practice in the field. Accordingly, I view tool building as a vital practice that is complementary to theoretical and methodological research. Throughout my own work I have contributed to the research community by developing several practical software tools, including BehaviorSearch (http://www.behaviorsearch.org/)
Complex adaptive systems, complexity, systems science, creativity, data mining, machine learning, economic and health systems, science education
Agent Based Modelling of energy consumer’s awareness diffusion. Role of smart metering in energy consumption. Social norm as limiting factor against rebound effects. Role of behavioral changes in energy efficiency.
Agent-based modeling and simulation of public policies.
Assistant Professor of Computer Science and Multimodal Transportation Systems
Modeling complex adaptive systems, graph theory, agent-based modeling, reinforcement learning, urban mobility, intelligent transport systems
Dissertation: Narrative Generation for Agent-Based Models
Abstract: This dissertation proposes a four-level framework for thinking about having agent-based models (ABM) generate narrative describing their behavior, and then provides examples of models that generate narrative at each of those levels. In addition, “interesting” agents are identified in order to direct the attention of researchers to the narratives most likely to be worth spending their time reviewing. The focus is on developing techniques for generating narrative based on agent actions and behavior, on techniques for generating narrative describing aggregate model behavior, and on techniques for identifying “interesting” agents. Examples of each of these techniques are provided in two different ABMs, Zero-Intelligence Traders (Gode & Sunder, 1993, 1997) and Sugarscape (Epstein & Axtell, 1996).
Displaying 10 of 87 results computer science clear search