16870249
Selecting Degenerate Multiplex PCR Primers
Single Nucleotide Polymorphism (SNP) Genotyping is an important molecular genetics technique in the early stages of producing results that will be useful in the medical field. One of the proposed methods for performing SNP Genotyping requires amplifying regions of DNA surrounding a large number of SNP loci. In order to automate a portion of this method and make the use of SNP Genotyping more widespread, it is important to select a set of primers for the experiment. Selecting these primers can be formulated as the Multiple Degenerate Primer Design (MDPD) problem. An iterative beam-search algorithm, Multiple, Iterative Primer Selector (MIPS), is presented for MDPD. Theoretical and experimental analyses show that this algorithm performs well compared to the limits of degenerate primer design and the number of spurious amplifications should be small. Furthermore, MIPS outperforms an existing algorithm which was designed for a related degenerate primer selection problem.
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.
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.
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.
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.
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.
14044536
Speeding Up the Convergence of Value Iteration in Partially Observable Markov Decision Processes
Partially observable Markov decision processes (POMDPs) have recently become popular among many AI researchers because they serve as a natural model for planning under uncertainty. Value iteration is a well-known algorithm for finding optimal policies for POMDPs. It typically takes a large number of iterations to converge. This paper proposes a method for accelerating the convergence of value iteration. The method has been evaluated on an array of benchmark problems and was found to be very effective: It enabled value iteration to converge after only a few iterations on all the test problems.
13348734
Growing genetic regulatory networks from seed genes
MOTIVATION
A number of models have been proposed for genetic regulatory networks. In principle, a network may contain any number of genes, so long as data are available to make inferences about their relationships. Nevertheless, there are two important reasons why the size of a constructed network should be limited. Computationally and mathematically, it is more feasible to model and simulate a network with a small number of genes. In addition, it is more likely that a small set of genes maintains a specific core regulatory mechanism.


RESULTS
Subnetworks are constructed in the context of a directed graph by beginning with a seed consisting of one or more genes believed to participate in a viable subnetwork. Functionalities and regulatory relationships among seed genes may be partially known or they may simply be of interest. Given the seed, we iteratively adjoin new genes in a manner that enhances subnetwork autonomy. The algorithm is applied using both the coefficient of determination and the Boolean-function influence among genes, and it is illustrated using a glioma gene-expression dataset.


AVAILABILITY
Software for the seed-growing algorithm will be available at the website for Probabilistic Boolean Networks: http://www2.mdanderson.org/app/ilya/PBN/PBN.htm
43211087
Simplify Decision Function of Reduced Support Vector Machines
Reduced Support Vector Machines (RSVM) was proposed as the alternate of standard support vector machines (SVM) in order to resolve the difficulty in the learning of nonlinear SVM for large data set problems. RSVM preselects a subset as support vectors and solves a smaller optimization problem, and it performs well with remarkable efficiency on training of SVM for large problem. All the training points of the subset will be support vectors, and more training points are selected into this subset results in high possibility to obtain RSVM with better generalization ability. So we first obtain the RSVM with more support vectors, and selects out training examples near classification hyper plane. Then only these training examples are used as training set to obtain a standard SVM with less support vector than that of RSVM. Computational results show that standard SVMs on the basis of RSVM have much less support vectors and perform equal generalization ability to that of RSVM.
17966983
Flexible Workflow Driven Job Shop Manufacturing Execution and Automation Based on Multi Agent System
Manufacturing automation with adoptability to the changing environment is a competitive advantage for shop floors. This paper presents a flexible workflow enacted job shop manufacturing execution and automation system. The manufacturing process sequence of each part is modeled as an abstract workflow and each manufacturing job resulting in a batch of the part items is recognized as corresponding workflow instance. The production schedule is generated to establish executable workflow instances to be enacted by the workflow engine and executed by the resources in the shop floor. Agent technology is applied to construct the system to take the advantage of distributed system architecture, reactivity, adaptability and cooperation. The proposed system can help to achieve production efficiency without huge investment on industrial automation system. The system architecture and implementation methods are discussed with demonstration and the performance of manufacturing shop floor is enhanced after the application.
139970
Immune Clonal MO Algorithm for 0/1 Knapsack Problems
In this paper, we introduce a new multiobjective optimization (MO) algorithm to solve 0/1 knapsack problems using the immune clonal principle. This algorithm is termed Immune Clonal MO Algorithm (ICMOA). In ICMOA, the antibody population is split into the population of the nondominated antibodies and that of the dominated antibodied. Meanwhile, the nondominated antibodies are allowed to survive and to clone. A metric of Coverage of Two Sets is adopted for the problems. This quantitative metric is used for testing the convergence to the Pareto-optimal front. Simulation results on the 0/1 knapsack problems show that ICMOA, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared with SPEA, NSGA, NPGA and VEGA.