15388288
Performance of Linear-Space Search Algorithms
Abstract Search algorithms that use space linear in the search depth are widely employed in practice to solve difficult problems optimally, such as planning and scheduling. In this paper, we study the average-case performance of linear-space search algorithms, including depth-first branch-and-bound (DFBnB), iterative-deepening (ID), and recursive best-first search (RBFS). To facilitate our analyses, we use a random tree T(b,d) that has mean branching factor b , depth d , and node costs that are the sum of the costs of the edges from the root to the nodes. We prove that the expected number of nodes expanded by DFBnB on a random tree is no more than bd times the expected number of nodes expanded by best-first search (BFS) on the same tree, which usually requires space that is exponential in depth d . We also show that DFBnB is asymptotically optimal when BFS runs in exponential time, and ID and RBFS are asymptotically optimal when the edge costs of T ( b , d ) are integers. If bp 0 is the expected number of children of a node whose costs are the same as that of their parent, then the expected number of nodes expanded by these three linear-space algorithms is exponential when bp o d 4 ) when bp 0 = 1, and at most quadratic when bp 0 > 1. In addition, we study the heuristic branching factor of T ( b , d ) and the effective branching factor of BFS, DFBnB, ID, and RBFS on T ( b , d ). Furthermore, we use our analytic results to explain a surprising anomaly in the performance of these algorithms, and to predict the existence of a complexity transition in the Asymmetric Traveling Salesman Problem.
12824361
Data Mining Methods for Modeling Gene Expression Regulation and Their Applications
This paper demonstrates machine learning and data mining methods that can be developed and applied to analyzing large quantities of genomic information and gene expression data for characterizing and modeling gene expression regulation. In particular, there will be a discussion on some of the methods that have been developed for modeling gene expression regulation underlying abiotic stress (e.g., drought, low temperature and salinity) tolerance, for identifying gene responsive to particular environmental stress conditions, and for characterizing the functions of microRNA genes for stress regulation in model plant Arabidopsis thaliana.
3517573
A template-based and pattern-driven approach to situation awareness and assessment in virtual humans
Situation awareness and assessment are fundamental components of rational virtual humans. They are central parts of models of virtual humans and critical elements of efficient reasoning and planning systems. In this paper, we describe our efforts of developing the capability of situation awareness in autonomous, synthetic virtual pilots in a military domain. After briefly describing the motivations of this research, we present an agent architecture that integrates perception, reasoning with situation awareness, and actions. We describe a representation of situations, methods for situation assessment, and applications of situation awareness in information collection, and creating focused attention. The quantitative measure from our experiment shows that by using focused attention based on the ability of situation awareness, the perceptional load of virtual pilots is significantly reduced, reducing pilots’ response time by more than 50 percent.
2426741
An analysis and application of distributed constraint satisfaction and optimization algorithms in sensor networks
This research is motivated by a distributed scheduling problem in distributed sensor networks, in which computational resources are scarce. To cope with limited computational resources and restricted real-time requirement, it is imperative to apply distributed algorithms that have low overhead on resource requirement and high anytime performance. In this paper, We study distributed stochastic algorithm (DSA) and distributed breakout algorithm (DBA), two distributed algorithms developed earlier for distributed constraint satisfaction problems. We experimentally investigate their properties and compare their performance using our distributed scheduling problem as a benchmark. We first formulate the scheduling problem as a distributed multi-coloring problem. We then experimentally show that the solution quality and communication cost of DSA exhibit phase-transition or threshold behavior, in that the performance degenerates abruptly and dramatically when the degree of parallel executions of distributed agents increases beyond some critical value. The results show that when controlled properly, DSA is superior to DBA, having better or competitive solution quality and significantly smaller communication cost than DBA. Therefore, DSA is the algorithm of choice for our distributed scan scheduling problem.
205692410
Average-case analysis of best-first search in two representative directed acyclic graphs
Many problems that arise in the real world have search spaces that are graphs rather than trees. Understanding the properties of search algorithms and analyzing their performance have been major objectives of research in AI. But most published work on the analysis of search algorithms has been focused on tree search, and comparatively little has been reported on graph search. One of the major obstacles in analyzing average-case complexity of graph search is that no single graph can serve as a suitable representative of graph search problems. In this paper we propose one possible approach to analyzing graph search. We take two problem domains for which the search graphs are directed acyclic graphs of similar structure, and determine the average case performance of the best-first search algorithm A* on these graphs. The first domain relates to one-machine job sequencing problems in which a set of jobs must be sequenced on a machine in such a way that a penalty function is minimized. The second domain concerns the Traveling Salesman Problem. Our mathematical formulation extends a technique that has been used previously for analyzing tree search. We demonstrate the existence of a gap in computational cost between two classes of problem instances. One class has exponential complexity and the other has polynomial complexity. For the job sequencing domain we provide supporting experimental evidence showing that problems exhibit a huge difference in computational cost under different conditions. For the Traveling Salesman Problem, our theoretical results reflect on the long-standing debate on the expected complexity of branch-and-bound algorithms for solving the problem, indicating that the complexity can be polynomial or exponential, depending on the accuracy of the heuristic function used.
10248617
Towards flexible teamwork in persistent teams
Teamwork is a critical capability in multi-agent environments. Many such environments mandate that the agents and agent-teams mast be persistent i.e., exist over long periods of time. Agents in such persistent teams are bound together by their long-term common interests and goals. This paper focuses on flexible teamwork in such persistent teams. Unfortunately, while previous work has investigated flexible teamwork, persistent teams remain unexplored. For flexible teamwork, one promising approach that has emerged is model-based, i.e., providing agents with general models of teamwork that explicitly specify their commitments in teamwork. Such models enable agents to autonomously reason about coordination. Unfortunately, for persistent teams, such models may lead to coordination and communication actions that while locally optimal, are highly problematic for the team's long-term goals. We present a decision-theoretic technique to enable persistent teams to overcome such limitations of the model-based approach. In particular, agents reason about expected team utilities of future team states that are projected to result from actions recommended by the teamwork model, as well as lower-cost (or higher-cost) variations on these actions. To accommodate real-time constraints, this reasoning is done in an any-time fashion. Implemented examples from an analytic search tree and some real-world domains are presented.
14318300
Features for neural net based region identification of newspaper documents
Several features for neural network based document region identification are tested. Specifically, this paper examines features for non-text region identification. The neural network based region identification algorithm is a key component of a document recognition system that segments a document into regions, classifies them into text, graphic, photo, and other region types, and then uses this classification to guide the processing and analysis of the image. The input data are unusually challenging: low quality images of newspaper documents obtained from microfilmed archives. The results compare favorably with other results reported in the literature.
45074176
Knowledge Adoption in Online Communities of Practice
Although researchers and practitioners have recognized that online Communities of Practice (COPs) can be valuable tools for knowledge management, there is little quantitative research about this relatively new phenomenon. This dissertation helps fill the gap by investigating how individual members of text-based, asynchronous online COPs adopt knowledge contributed by others for their own problem solving. 
For this purpose, this dissertation methodologically utilizes dual-process theories of information processing. It first verifies the appropriateness of the theories by replicating previous research in the new domain. Then, the behaviors and effects of factors that are not previously researched in this way, yet especially pertain to online COPs, are investigated. It is hypothesized that genre conformity and information consistency operate as two heuristic cues in the knowledge adoption in online COPs. It is also hypothesized that focused search and the presence of disconfirming information increase elaboration likelihood during dual-process knowledge adoption. These hypotheses were tested and partially supported with survey data collected from members of two online COPs with different characteristics. 
This dissertation contributes to both IS research and the dual-process theories. Contrary to the cue-filtering paradigm of previous media research, it expands the computer-mediated communication (CMC) literature by identifying additional cues that are utilized for CMC-based knowledge adoption. It invokes the notion that the CMC context plays a dynamic role in ongoing knowledge adoption processes, both through elaboration likelihood and through interactions between the dual processes. Moreover, it advances the situated view of knowledge adoption by demonstrating that how members search for information and how current information compares to prior knowledge matter in online COPs. Focusing on the pull technology of online COPS, it offers another theoretical link between CMC technologies and knowledge management. 
Practically, this dissertation provides us with new insights into the utilization of online COPs for improving organizational knowledge management. It also suggests new directions for designing software systems to better support knowledge sharing in online COPs.
2889342
Software cost estimation through conceptual requirement
Software cost estimation is vital for the effective control and management of the whole software development process. Currently, the constructive cost model (COCOMO II) is the most popular tool for estimating software cost. It uses lines of code and function points to assess software size. However, these are actually implementation details and difficult to estimate during the early stage of software development. The entity relationship (ER) model is well used in conceptual modeling (requirements analysis) for data-intensive systems. In this article, we explore the use of ER model for the estimation of software cost. A new term, path complexity, is proposed. Based on path complexity and other factors, we built a multiple regression model for software cost estimation. The approach has been validated statistically through system data from the real industry projects.
12895493
Improvement of HITS-based algorithms on web documents
In this paper, we present two ways to improve the precision of HITS-based algorithms on Web documents. First, by analyzing the limitations of current HITS-based algorithms, we propose a new weighted HITS-based method that assigns appropriate weights to in-links of root documents. Then, we combine content analysis with HITS-based algorithms and study the effects of four representative relevance scoring methods, VSM, Okapi, TLS, and CDR, using a set of broad topic queries. Our experimental results show that our weighted HITS-based method performs significantly better than Bharat's improved HITS algorithm. When we combine our weighted HITS-based method or Bharat's HITS algorithm with any of the four relevance scoring methods, the combined methods are only marginally better than our weighted HITS-based method. Between the four relevance-scoring methods, there is no significant quality difference when they are combined with a HITS-based algorithm.
18427300
Exploring Functional Unit Design Space of VLIW Processors for Optimizing Both Performance and Energy Consumption
The number of functional units can have significant impact on both the performance and energy consumption of VLIW processors. This paper uses a design exploration approach to find optimal integer functional unit configurations for achieving the best EDP (energy delay product) results for different media applications. Our experimental results quantitatively indicate that the optimal number of integer functional units should match the instruction level parallelism that can be extracted from the applications to balance both performance and energy optimally for VLIW processors.