IMPACT

1. Less constrained Transformation and constraint relaxation Global state estimator Difficult phase Environment Probably solvable Unsolvable within bounds Progress monitor Problem solver progress Flexible Methods for Multi-Agent Distributed Resource Allocations by Exploiting Phase Transitions (DCMP) PHASE-AWARE PROBLEM SOLVING NEW IDEAS • Modeling distributed resource allocation problem (DRAP) as distributed constraint minimization problem (DCMP) • Introducing weights (e.g., importance) to constraints • Finding solutions with minimal weights • Discovering and charactering phase transitions in DCMP and DRAP and constructing phase diagrams • Estimating and predicting global states using local information • Problem transformation methods that exploit phase transitions for developing distributed anytime algorithms • Parametric transformation: manipulate constraint parameters • Structural transformation: altering constraint structures • Iterative algorithms for progressive improvement SCHEDULE IMPACT Structural transformation • Understanding and theoretical characterization of the dynamics and computational complexity of distributed resource allocation problems • Providing guidelines for designing and developing high performance multi-agent systems and agent negotiation strategies • Demonstration of innovative, phase-aware distributed problem-solving methods for finding satisfactory solutions within limited resource bounds Parametric transformation Demo on challenge problems Global state estimator Phase transitions Initial working system Modeling DRAP Complete models, phase transition results Year 1 Year 2 Year 3 USC/Information Sciences Institute: Weixiong Zhang (PI)