Reasoning and Planning in Multi-Agent Environment
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Our goal is to develop an action language that can be used for representing and reasoning about knowledge and beliefs of agents in a multi-agent system. The language needs to be able to deal with knowledge producing actions, various types of announcement actions (private vs public, lying, misleading) and can also be used as a specification language for epistemic planning. |
Preference Elicitation and Device Scheduling for Smart Homes
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A home automation system (HAS) should have three functions: (1) Eliciting the preference and constraints of users in the home; (2) Constructing a schedule for the smart devices in the home in order to satisfy the user constraints as well as minimize energy costs for the user; and (3) Proposing and explaining the schedule to the users in an intuitive way and considers their suggested changes to the schedules. |
Constraint Programming
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Constraint Programming (CP) is an software technology for declarative description and effective solving of large, particularly combinatorial problems, especially in areas of planning and scheduling. Constraints arise in most areas of human endeavour. They formalise the dependencies in physical worlds and their mathematical abstractions is simply a logical relation among several unknowns (or variables), each taking a value in a given domain. The constraint thus caputres partial information about the variables of interest and restricts the possible values that variables can take. The important feature of constraints is their declarative manner, i.e., they specify what relationship must hold without specifying a computational procedure to enforce that relationship. |
Distributed Constraint Optimization
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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. |
Probabilistic Planning
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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.
Probabilistic planning is an extension of nondeterministic planning with information on the probabilities of nondeterministic events. 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 (Dec-POMDP) is an extension where a team of agents needs to collectively plan their joint actions.
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Research Awards
RECENT GRANTS
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- iCREDITS: interdisciplinary Center of Research Excellence in Design of Intelligent Technologies for Smartgrids (2014 — 2019)
- Department PI(s): Enrico Pontelli, William Yeoh, and Satyajayant (Jay) Misra
- Funding Agency: National Science Foundation (NSF)
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- MRI: Acquisition of an Instrument for Research in Irregularly Parallel Big Data Computation (2013 — 2016)
- Department PI(s): Jonathan Cook, Enrico Pontelli, Mingzhou (Joe) Song, and Huiping Cao
- Funding Agency: National Science Foundation (NSF)
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- Graduate Assistants in Areas of National Need (GAANN) (2012 — 2015)
- Department PI(s): Jonathan Cook, Satyajayant (Jay) Misra, and Enrico Pontelli
- Funding Agency: Department of Education (DoE)
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- New, GK-12: Computing in Context: Advancing Computational Thinking in the Classroom through Applied Computational Research (2010 — 2015)
- Department PI(s): Enrico Pontelli and Jonathan Cook
- Funding Agency: National Science Foundation (NSF)
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- BPC-AE: Computing Alliance of Hispanic-Serving Institutions (2010 — 2015)
- Department PI(s): Enrico Pontelli
- Funding Agency: National Science Foundation (NSF)
INTERNATIONAL AWARDS
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- Best Student Paper Award, Conference on Computational Methods in Systems Biology (CMSB) — 2013
- Authors: Ferdinando Fioretto and Enrico Pontelli
- Title: Constraint Programming in Community-based Gene Regulatory Network Inference
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- Best Student Paper Award, International Conference on Automated Planning and Scheduling (ICAPS) — 2012
- Authors: Khoi Nguyen, Vien Tran, Son Tran, and Enrico Pontelli
- Title: On Computing Conformant Plans Using Classical Planners: A Generate-And-Complete Approach
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- Best Paper Award, International Conference on Logic Programming (ICLP) — 2010
- Authors: Alessandro Dal Palù, Agostino Dovier, Federico Fogolari, and Enrico Pontelli
Title: CLP-Based Protein Fragment Assembly
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- Best Planner Award (Non-Observable Non-Deterministic Track), International Planning Competition — 2008
- Authors: Vien Tran, Khoi Nguyen, Son Tran, and Enrico Pontelli
- Title: Conformant Planning Based on Approximation
DEPARTMENTAL AWARDS
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- Outstanding Teaching Assistant Awards
- Chuan Hu and Reza Tourani — Spring 2014
- Ben Wright — Fall 2013
- Khoi Nguyen — Spring 2013
- Hieu Nguyen and Tiep Le — Fall 2012
- Ferdinando Fioretto — Spring 2012
- Hieu Nguyen — Spring 2011
- Nancy Alajarmeh — Fall 2010
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- Outstanding Research Assistant Awards
- Ferdinando Fioretto — 2013
- Son Thanh To — 2012
- Khoi Nguyen — 2011
- Son Thanh To — 2010