My research interests are in artificial intelligence, primarily in the areas of constraint-based search and optimization, automated planning and scheduling, configurable and adaptive problem solving systems, multi-agent and multi-robot coordination, mixed-initiative decision-making, and naturally inspired search procedures. One integrating focus has been the development of core technologies for coordination and control of large-scale, multi-actor systems, and their application in domains such as manufacturing, transportation, logistics, space mission planning, and eBusiness.
Practical Planning and Scheduling
I am interested broadly in the theory and practice of next-generation technologies for practical planning and scheduling. One thread of current research focuses on algorithms for constructing robust plans and schedules, which capture sets of possible execution futures and anticipate executional uncertainty. Another topic of current research is over-subscribed planning and scheduling, which involves problems where available resources preclude accomplishment off all goals and solutions must therefore maximize expected gain. Other general interests here include constraint-based planning and scheduling, integrated action selection and resource allocation, visualization and mixed-initiative manipulation of plans/schedules, reactive plan/schedule repair, planning and scheduling under complex (and potentially conflicting) constraints, and planning/scheduling search-space analysis.
Coordinating Distributed Planning and Scheduling Agents
Increasingly, my research has emphasized planning and scheduling problems that are inherently distributed and require mechanisms for coordinated decision-making by multiple agents. One current focus (within the Coordinators project) is on distributed execution and management of joint schedules in an uncertain execution environment, where each agent has responsibility for carrying out some portion of an overall operation, the actions of different agents are inter-dependent, but no one agent has a complete global view. Other current work is investigating algorithms for distributed use of 3D space over time by multiple air vehicles. My broader research interests here include self-scheduling systems, distributed constraint optimization, and negotiation-based approaches to distributed planning and scheduling.
Adaptive and Configurable Problem Solvers
Another general research interest is the design of configurable and adaptive systems. One area of current research focuses on mechanisms for exploiting the use of multiple heuristics to efficiently solve planning, scheduling and optimization problems. Topics here include online learning strategies for allocating trials to heuristics within iterative sampling search procedures, algorithm portfolio design, and other adaptive search procedures. A second area of current research focuses on planning and scheduling assistants that learn user preferences over time. At another level, I am also interested in the development of reconfigurable planning and scheduling system architectures, which promote rapid development of high performance application systems.
Finally, I interested broadly in the design and use of genetic algorithms (GAs) and other related evolutionary computation models, as well as other biologically inspired computational mechanisms. Some specific research areas of interest include: the design of GA-based architectures for learning rule-based decision models from payoff-based feedback about past performance; the design of (heuristic) search operators for non-standard GA problem representations; inter-operability of population-based search with other (possibly domain specific) optimization algorithms and heuristics; and dynamic adaptive control of complex multi-agent systems.
I am involved in Traffic21, a transportation research initiative of Carnegie Mellon University. Its goal is to design, test, deploy and evaluate information and communications technology based solutions to address the problems facing the the transportation system of the Pittsburgh region. Within Traffic21, my work focuses on adaptive traffic signal control strategies and dynamic, real-time scheduling of paratransit operations.
|Research Interest Keywords|
|artificial intelligence, constraint-directed reasoning, distributed problem solving, factory and warehouse automation, human-computer interaction, intelligent transportation, machine learning, manufacturing, multi-agent systems, optimization, planning, scheduling|
|The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.|
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