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Jun-young Kwak

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8 papers
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8

JAAMAS Journal 2013 Journal Article

TESLA: an extended study of an energy-saving agent that leverages schedule flexibility

  • Jun-young Kwak
  • Pradeep Varakantham
  • Wendy Wood

Abstract This paper presents transformative energy-saving schedule-leveraging agent (TESLA), an agent for optimizing energy usage in commercial buildings. TESLA’s key insight is that adding flexibility to event/meeting schedules can lead to significant energy savings. This paper provides four key contributions: (i) online scheduling algorithms, which are at the heart of TESLA, to solve a stochastic mixed integer linear program for energy-efficient scheduling of incrementally/dynamically arriving meetings and events; (ii) an algorithm to effectively identify key meetings that lead to significant energy savings by adjusting their flexibility; (iii) an extensive analysis on energy savings achieved by TESLA; and (iv) surveys of real users which indicate that TESLA’s assumptions of user flexibility hold in practice. TESLA was evaluated on data gathered from over 110, 000 meetings held at nine campus buildings during an 8-month period in 2011–2012 at the University of Southern California and Singapore Management University. These results and analysis show that, compared to the current systems, TESLA can substantially reduce overall energy consumption.

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AAMAS Conference 2012 Conference Paper

Multiagent Systems for Sustainable Energy Applications

  • Jun-young Kwak

Sustainable energy domains have become extremely important due to the significant growth in energy usage. Building multiagent systems for real world energy applications raises several research challenges regarding scalability, multiple competing objectives to be optimized, model uncertainty, and complexity in deploying the system. Motivated by these challenges, my thesis proposes a new set of models and algorithms to conserve building energy. My thesis contributes to a very new area that requires considering largescale multi-objective optimization as well as uncertainty over occupant preferences when negotiating energy reduction. My work has shown significant potential for energy savings by investigating effective and tailored methods in the multiagent system. The suggested methods have been verified in a validated simulation testbed and included a human subject study in the real-world as a trial study.

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AAMAS Conference 2012 Conference Paper

SAVES: A Sustainable Multiagent Application to Conserve Building Energy Considering Occupants

  • Jun-young Kwak
  • Pradeep Varakantham
  • Rajiv Maheswaran
  • Milind Tambe
  • Farrokh Jazizadeh
  • Geoffrey Kavulya
  • Laura Klein
  • Burcin Becerik-Gerber

This paper describes an innovative multiagent system called SAVES with the goal of conserving energy in commercial buildings. We specifically focus on an application to be deployed in an existing university building that provides several key novelties: (i) jointly performed with the university facility management team, SAVES is based on actual occupant preferences and schedules, actual energy consumption and loss data, real sensors and hand-held devices, etc. ; (ii) it addresses novel scenarios that require negotiations with groups of building occupants to conserve energy; (iii) it focuses on a non-residential building, where human occupants do not have a direct financial incentive in saving energy and thus requires a different mechanism to effectively motivate occupants; and (iv) SAVES uses a novel algorithm for generating optimal MDP policies that explicitly consider multiple criteria optimization (energy and personal comfort) as well as uncertainty over occupant preferences when negotiating energy reduction - this combination of challenges has not been considered in previous MDP algorithms. In a validated simulation testbed, we show that SAVES substantially reduces the overall energy consumption compared to the existing control method while achieving comparable average satisfaction levels for occupants. As a real-world test, we provide results of a trial study where SAVES is shown to lead occupants to conserve energy in real buildings.

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AAMAS Conference 2011 Conference Paper

Teamwork in Distributed POMDPs: Execution-time Coordination Under Model Uncertainty

  • Jun-young Kwak
  • Rong Yang
  • Zhengyu Yin
  • Matthew E. Taylor
  • Milind Tambe

Despite their worst-case NEXP-complete planning complexity, DEC-POMDPs remain a popular framework for multiagent teamwork. This paper introduces effective teamwork under model uncertainty (i. e. , potentially inaccurate transition and observation functions) as a novel challenge for DEC-POMDPs and presents MODERN, the first execution-centric framework for DEC-POMDPs explicitly motivated by addressing such model uncertainty. MODERN's shift of coordination reasoning from planning-time to execution-time avoids the high cost of computing optimal plans whose promised quality may not be realized in practice. There are three key ideas in MODERN: (i) it maintains an exponentially smaller model of other agents' beliefs and actions than in previous work and then further reduces the computation-time and space expense of this model via bounded pruning; (ii) it reduces execution-time computation by exploiting BDI theories of teamwork, and limits communication to key trigger points; and (iii) it limits its decision-theoretic reasoning about communication to trigger points and uses a systematic markup to encourage extra communication at these points - thus reducing uncertainty among team members at trigger points.

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AAAI Conference 2010 Conference Paper

Urban Security: Game-Theoretic Resource Allocation in Networked Domains

  • Jason Tsai
  • Zhengyu Yin
  • Jun-young Kwak
  • David Kempe
  • Christopher Kiekintveld
  • Milind Tambe

Law enforcement agencies frequently must allocate limited resources to protect targets embedded in a network, such as important buildings in a city road network. Since intelligent attackers may observe and exploit patterns in the allocation, it is crucial that the allocations be randomized. We cast this problem as an attacker-defender Stackelberg game: the defender’s goal is to obtain an optimal mixed strategy for allocating resources. The defender’s strategy space is exponential in the number of resources, and the attacker’s exponential in the network size. Existing algorithms are therefore useless for all but the smallest networks. We present a solution approach based on two key ideas: (i) A polynomial-sized game model obtained via an approximation of the strategy space, solved efficiently using a linear program; (ii) Two efficient techniques that map solutions from the approximate game to the original, with proofs of correctness under certain assumptions. We present in-depth experimental results, including an evaluation on part of the Mumbai road network.

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ICAPS Conference 2009 Conference Paper

Exploiting Coordination Locales in Distributed POMDPs via Social Model Shaping

  • Pradeep Varakantham
  • Jun-young Kwak
  • Matthew E. Taylor
  • Janusz Marecki
  • Paul Scerri
  • Milind Tambe

Distributed POMDPs provide an expressive framework for modeling multiagent collaboration problems, but NEXP-Complete complexity hinders their scalability and application in real-world domains. This paper introduces a subclass of distributed POMDPs, and TREMOR, an algorithm to solve such distributed POMDPs. The primary novelty of TREMOR is that agents plan individually with a single agent POMDP solver and use social model shaping to implicitly coordinate with other agents. Experiments demonstrate that TREMOR can provide solutions orders of magnitude faster than existing algorithms while achieving comparable, or even superior, solution quality.

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