3005507
Iterative state-space reduction for flexible computation
Abstract Flexible computation is a general framework for decision making under limited computational resources. It enables an agent to allocate limited computational resources to maximize its overall performance or utility. In this paper, we present a strategy for flexible computation, which we call iterative state-space reduction . The main ideas are to reduce a problem space that is difficult to search to one that is relatively easy to explore, to use the optimal solution from the reduced space as an approximate solution to the original problem, and to iteratively apply multiple reductions to progressively find better solutions. The basic operation for state-space reduction is heuristic node pruning, which excludes the nodes that do not seem to lead to high-quality solutions from further examination. Iterative state-space reduction can be combined with a state-space search, such as best-first search or depth-first branch-and-bound (DFBnB). The resulting algorithm can run as an anytime algorithm, which provides a tradeoff between solution quality and computation. Based on an analytical modal, we analyze the probability that one iteration of the iterative process finds a solution. Furthermore, by combining it within DFBnB we apply iterative state-space reduction to three combinatorial problems, the maximum Boolean satisfiability, the symmetric TSP and asymmetric TSP. Our experimental results show that iterative state-space reduction is effective and efficient, making DFBnB find better solutions with less computation.
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.
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.
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.
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.
9354212
High-Performance Job-Shop Scheduling With A Time-Delay TD-lambda Network
Job-shop scheduling is an important task for manufacturing industries. We are interested in the particular task of scheduling payload processing for NASA's space shuttle program. This paper summarizes our previous work on formulating this task for solution by the reinforcement learning algorithm T D(). A shortcoming of this previous work was its reliance on hand-engineered input features. This paper shows how to extend the time-delay neural network (TDNN) architecture to apply it to irregular-length schedules. Experimental tests show that this TDNN-TD() network can match the performance of our previous hand-engineered system. The tests also show that both neural network approaches signiicantly out-perform the best previous (non-learning) solution to this problem in terms of the quality of the resulting schedules and the number of search steps required to construct them.
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.
35045189
New Born Radii Deriving Method for Generalized Born Model
Here we report a method to calculate Born radii, an important parameter used in a Generalized Born model. Traditional methods to derive Born radii are mostly based on a complicated formula, while our method is easier and more direct. Atoms are classified according to their atom type, and the Born radii of each type are obtained by fitting to experimental solvation free energy. The SMARTS language is used for the exact definition of atoms types, and Ullmann's subgraph isomorphism algorithm is used to deduce the environment. A generic algorithm is used for the parameter fitting because of its efficiency in searching a huge phase space, and its results are then optimized by using the conjugate gradient method. The final parameter set is fitting from a training set containing 357 molecules and is tested using a test set of 44 small organic molecules, and the average error is 0.58 kcal/mol for 36 neutral molecules and is 1.67 kcal/mol for 8 ions. The model is further tested under organic molecules, biopolymers, and a protein-inhibitor complex and yields reliable results in all these cases. This method can be used to accelerate molecular docking calculations.
33837023
Comments on "Maximum diversity in single-carrier frequency-domain Equalization"
It was claimed in the above correspondence that "single-carrier frequency-domain equalization (SC-FDE) is capable of collecting full multipath diversity even without channel coding." However, this is not correct because Proposition 2 and Theorem 1 of the above correspondence, which lead to the claim, are both incorrect. In this correspondence, Proposition 2 and Theorem 1 are disproved. As a consequence, the main claim of the above correspondence is denied. Furthermore, a new theorem is given to show that the multipath diversity cannot be achieved by SC-FDE without channel coding. An example is also presented to disprove the main claim of of the above correspondence meanwhile supporting the new theorem proposed in this correspondence
28591822
Multicast Routing with Minimum Energy Cost and Minimum Delay in Wireless Sensor Networks
In this paper, we consider the multicast routing with minimum energy cost and minimum delay (MEMD) in wireless sensor networks under the access control scheme of Spatial Time Division Multiple Access (STDMA). We formulate and explore both MEMD multicast and ME multicast, and show that the latter is just the Maximum Leaf Spanning Tree (MLST) problem, which is NP-complete. A 2-approximation algorithm is proposed for the MLST problem through improving a known one. Based on this algorithm, an approximation algorithm is obtained for the MEMD multicast problem. To further improve the delay result, we provide another approximation algorithm for our main problem using a different approach. These algorithms are all near linear in the size of the network graph, and also are shown to have good performance by simulation results.