Jeffrey Hudack

Multiobjective Optimization for the Stochastic Physical Search Problem

We model an intelligence collection activity as multiobjective optimization on a binary stochastic physical search problem, providing formal definitions of the problem space and nondominated solution sets. We present the Iterative Domination Solver as an approximate method for generating solution sets that can be used by a human decision maker to meet the goals of a mission. We show that our approximate algorithm performs well across a range of uncertainty parameters, with orders of magnitude less execution time than existing solutions on randomly generated instances.

Exact and Heuristic Algorithms for Risk‐Aware Stochastic Physical Search

We consider an intelligent agent seeking to obtain an item from one of several physical locations, where the cost to obtain the item at each location is stochastic. We study risk‐aware stochastic physical search (RA‐SPS), where both the cost to travel and the cost to obtain the item are taken from the same budget and where the objective is to maximize the probability of success while minimizing the required budget. This type of problem models many task‐planning scenarios, such as space exploration, shopping, or surveillance. In these types of scenarios, the actual cost of completing an objective at a location may only be revealed when an agent physically arrives at the location, and the agent may need to use a single resource to both search for and acquire the item of interest. We present exact and heuristic algorithms for solving RA‐SPS problems on complete metric graphs. We first formulate the problem as mixed integer linear programming problem. We then develop custom branch and bound algorithms that result in a dramatic reduction in computation time. Using these algorithms, we generate empirical insights into the hardness landscape of the RA‐SPS problem and compare the performance of several heuristics.

Virtual Structure Reduction on Distributed K-Coloring Problems

Distributed Constraint Problem solving represents a fundamental research area in distributed artificial intelligence and multi-agent systems. The constraint density, or the ratio of the number of constraints to the number of variables, determines the difficulty of either finding a solution or minimizing the set of variable assignment conflicts in a constraint problem. Reducing the active density of a problem typically reduces difficulty in finding a solution. We present a fully distributed technique for reducing the active density of constraint graphs that represent Distributed Constraint Optimization Problems (DCOP), called Virtual Structure Reduction (VSR). The VSR technique leverages the occurrence of frozen pairs, or variables that must be assigned the same value based on shared constraints between variables within the local neighborhood. We show how frozen pairs can be used to identify surrogate relationships between variables which reduces the active set of nodes in the constraint graph and leads to a reduction in active density. We discuss our DCOP solver, integrated with the Distributed Stochastic Algorithm (DSA), called VSR-DSA. The VSR-DSA algorithm demonstrates significant performance gains compared to the DSA-B and MGM algorithms in messages, cycles, solution quality, and time on randomized instances of the 3-coloring problem.