David C. Uthus

Multiparticipant chat analysis: a survey

We survey research on the analysis of multiparticipant chat. Multiple research and applied communities (e.g., AI, educational, law enforcement, military) have interest in this topic. After introducing some context, we describe relevant problems and how these have been addressed using AI techniques. We also identify recent research trends and unresolved issues that could benefit from more attention.

DFS* and the Traveling Tournament Problem

Our paper presents a new exact method to solve the traveling tournament problem. More precisely, we apply DFS* to this problem and improve its performance by keeping the expensive heuristic estimates in memory to help greatly cut down the computational time needed. We further improve the performance by exploiting a symmetry property found in the traveling tournament problem. Our results show that our approach is one of the top performing approaches for this problem. It is able to find known optimal solutions in a much smaller amount of computational time than past approaches, to find a new optimal solution, and to improve the lower bounds of larger problem instances which do not have known optimal solutions. As a final contribution, we also introduce a new set of problem instances to diversify the available instance sets for the traveling tournament problem.

An ant colony optimization approach to the traveling tournament problem

The traveling tournament problem has proven to be a difficult problem for the ant colony optimization metaheuristic, with past approaches showing poor results. This is due to the unusual problem structure and feasibility constraints. We present a new ant colony optimization approach to this problem, hybridizing it with a forward checking and conflict-directed backjumping algorithm while using pattern matching and other constraint satisfaction strategies. The approach improves on the performance of past ant colony optimization approaches, finding better quality solutions in shorter time, and exhibits results comparable to other state-of-the-art approaches.

Solving the traveling tournament problem with iterative-deepening A *

This work presents an iterative-deepening A∗ (IDA∗) based approach to the traveling tournament problem (TTP). The TTP is a combinatorial optimization problem which abstracts the Major League Baseball schedule. IDA∗ is able to find optimal solutions to this problem, with performance improvements coming from the incorporation of various past concepts including disjoint pattern databases, symmetry breaking, and parallelization along with new ideas of subtree skipping, forced deepening, and elite paths to help to reduce the search space. The results of this work show that an IDA∗ based approach can find known optimal solutions to most TTP instances much faster than past approaches. More importantly, it has been able to optimally solve two larger instances that have been unsolved since the problem’s introduction in 2001. In addition, a new problem set called GALAXY is introduced, using a 3D space to create a challenging problem set.

Ant Colony Optimization and the Single Round Robin Maximum Value Problem

In this paper, we apply the ant colony optimization metaheuristic to the Single Round Robin Maximum Value Problem, a problem from sports scheduling. This problem contains both feasibility constraints and an optimization goal. We approach this problem using a combination of the metaheuristic with backtracking search. We show how using constraint satisfaction techniques can improve the hybrids performance. We also show that our approach performs comparably to integer programming and better than tabu search when applied to the Single Round Robin Maximum Value Problem.

Reports of the AAAI 2011 conference workshops

The AAAI-11 workshop program was held Sunday and Monday, August 7–18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages; Analyzing Microtext; Applied Adversarial Reasoning and Risk Modeling; Artificial Intelligence and Smarter Living: The Conquest of Complexity; AI for Data Center Management and Cloud Computing; Automated Action Planning for Autonomous Mobile Robots; Computational Models of Natural Argument; Generalized Planning; Human Computation; Human-Robot Interaction in Elder Care; Interactive Decision Theory and Game Theory; Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action and Language; Lifelong Learning; Plan, Activity, and Intent Recognition; and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events.