A distributed constraint optimization problem (DCOP) is a problem where multiple agents coordinate with each other to take on values such that the sum of the resulting constraint costs, that are dependent on the values of the agents, is minimal. DCOPs are a popular way of formulating and solving multi-agent coordination problems such as the distributed scheduling of meetings, distributed coordination of unmanned air vehicles and the distributed allocation of targets in sensor networks. Privacy concerns in the scheduling of meetings and the limitation of communication and computation resources of each sensor in a sensor network makes centralized constraint optimization difficult. Therefore, the nature of these applications call for a distributed approach.
Many real-world planning problems occur in environments where there may be incomplete information or where actions may not always lead to the same results. Examples include planning for retirement, where the state of the economy in the future is uncertain, and planning in logistics, where the duration of travel between two cities is uncertain due to potential congestion.
A Markov decision process (MDP) is a popular framework for modeling decision making in these kinds of problems, where an agent needs to plan a sequence of actions that maximizes its chances of reaching its goal. A partially observable MDP (POMDP) is an extension where the world that the agent is operating in is only partially observable, and a decentralized (PO)MDP is an extension where a team of agents needs to collectively plan their joint actions.
Transitioning our aging power grid to a smart grid brings a number of benefits including increased efficiency and sustainability as renewable resources (e.g., solar and wind) are incorporated into the grid. Additionally, it will also improve reliability and resiliency as smart sensors will be deployed throughout the grid to detect, respond, and adapt to events such as faults and failures. Finally, through smart meters and other Internet-of-Things devices in smart homes, the smart grid will also bring economical benefits to both power producers and consumers, as producers can introduce real-time pricing to reduce peak power consumption, and home automation systems of consumers can adapt accordingly while satisfying the needs and constraints of the homeowners.
Edge computing is a method of optimizing cloud computing systems by performing data processing at the edge of the network, closer to the users and sources of data. As data processing is traditionally done in large data centers, typically located at the center of the network, the edge computing paradigm will reduce the communication bottleneck to the data centers, thereby improving overall performance. This becomes more important as the number of Internet-of-Things (IoT) devices increases.
Incremental search algorithms reuse information from previous searches to speed up the current search and solve search problems potentially much faster than solving them repeatedly from scratch. They are widely popular in solving dynamic path-planning problems such as navigation for unmanned ground vehicles and motion planning for articulated robots. For example, existing incremental search algorithms such as D* and D* Lite have been adapted for use with much success in various robotic applications including the Mars rovers and autonomous vehicles in the DARPA Urban Challenge.
Boeing (2019 – 2021).
RI: Small: Collaborative Research: Preference Elicitation and Device Scheduling for Smart Homes.
National Science Foundation (2018 – 2021).
Mission-oriented Adaptive Placement of Task and Data (MAP).
Defense Advanced Research Projects Agency (2017 – 2021).
CAREER: Decentralized Constraint-based Optimization for Multi-Agent Planning and Coordination.
National Science Foundation (2016 – 2021).
BSF: 2014012: Robust Solutions for Distributed Constraint Optimization Problems.
National Science Foundation (2015 – 2019).
iCREDITS: Interdisciplinary Center of Research Excellence in Design of Intelligent Technologies for Smartgrids.
National Science Foundation (2014 – 2019).
- Early Career Spotlight Talk, International Joint Conference on Artificial Intelligence (IJCAI) (2018)
- CAREER Award, National Science Foundation (2016)
- AI’s 10 to Watch, IEEE Intelligent Systems (2015)
- Outstanding Research Assistant Award, Computer Science Department, University of Southern California (2009)
- Best Student Paper Award Nomination for Caching Schemes for DCOP Search Algorithms, International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (2009)