Yoonheui Kim

AAAI Conference 2014 Conference Paper

DJAO: A Communication-Constrained DCOP Algorithm that Combines Features of ADOPT and Action-GDL

• Yoonheui Kim
• Victor Lesser

In this paper we propose a novel DCOP algorithm, called DJAO, that is able to efficiently find a solution with low communication overhead; this algorithm can be used for optimal and bounded approximate solutions by appropriately setting the error bounds. Our approach builds on distributed junction trees used in Action-GDL to represent independence relations among variables. We construct an AND/OR search space based on these junction trees. This new type of search space results in higher degrees for each OR node, consequently yielding a more efficient search graph in the distributed settings. DJAO uses a branch-and-bound search algorithm to distributedly find solutions within this search graph. We introduce heuristics to compute the upper and lower bound estimates that the search starts with, which is integral to our approach for reducing communication overhead. We empirically evaluate our approach in various settings.

AAMAS Conference 2013 Conference Paper

Biasing the Behavior of Organizationally Adept Agents

• Daniel Corkill
• Chongjie Zhang
• Bruno da Silva
• Yoonheui Kim
• Daniel Garant
• Victor R. Lesser
• Xiaoqin Zhang

An organizationally adept agent (OAA) adjusts its behavior when given annotated organizational guidelines. More importantly, it can also determine when such guidelines become ineffective and proactively adapt its behavior to better achieve organizational objectives. We present the high-level aspects of this architecture and analyze its effectiveness using call-center OAAs striving to extinguish fires in RoboCup Rescue scenarios.

AAMAS Conference 2012 Conference Paper

A Better Maximization Procedure For Online Distributed Constraint Optimization

• Yoonheui Kim
• Victor Lesser

AAMAS Conference 2011 Conference Paper

Effective Variants of Max-Sum Algorithm to Radar Coordination and Scheduling

• Yoonheui Kim
• Michael Krainin
• Victor Lesser

This work proposes new techniques for saving communication and computational resources when solving distributed constraint optimization problems in an environment where system hardware resources are clustered. Using a pre-computed policy and two phase propagation on Max-Sum algorithm, the system performance on Radar scheduling problem improves in terms of communication and computation.