18832812
Depth-First vs. Best-First Search: New Results
Best-first search (BFS) expands the fewest nodes among all admissible algorithms using the same cost function, but typically requires exponential space. Depth-first search needs space only linear in the maximum search depth, but expands more nodes than BFS. Using a random tree, we analytically show that the expected number of nodes expanded by depth-first branch-and-bound (DFBnB) is no more than O(d ċ N), where d is the goal depth and N is the expected number of nodes expanded by BFS. We also show that DFBnB is asymptotically optimal when BFS runs in exponential time. We then consider how to select a linear-space search algorithm, from among DFBnB, iterative-deepening (ID) and recursive best first search (RBFS). Our experimental results indicate that DFBnB is preferable on problems that can be represented by bounded-depth trees and require exponential computation; and RBFS should be applied to problems that cannot be represented by bounded-depth trees, or problems that can be solved in polynomial time.
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.
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.
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.
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.
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.
6822470
A Reinforcement Learning Approach to job-shop Scheduling
We apply reinforcement learning methods to learn domain-specific heuristics for job shop scheduling. A repair-based scheduler starts with a critical-path schedule and incrementally repairs constraint violations with the goal of finding a short conflict-free schedule. The temporal difference algorithm TD(λ) is applied to tram a neural network to learn a heuristic evaluation function over states. This evaluation function is used by a one-step lookahead search procedure to find good solutions to new scheduling problems. We evaluate this approach on synthetic problems and on problems from a NASA space shuttle pay load processing task. The evaluation function is trained on problems involving a small number of jobs and then tested on larger problems. The TD scheduler performs better than the best known existing algorithm for this task--Zwehen's iterative repair method based on simulated annealing. The results suggest that reinforcement learning can provide a new method for constructing high-performance scheduling systems.
26485902
Identification of Crosscutting Requirements Based on Feature Dependency Analysis
Identification of crosscutting concerns at the requirements level is important for the modularization and evolution of requirements, and has attracted many research interests. This paper proposes a feature-oriented approach to the identification of crosscutting requirements based on feature dependency analysis. In this approach, features are used as basic elements to organize the requirements space, and two kinds of dynamic dependencies (i.e. interactions and weavings) between features are analyzed to find out the candidate crosscutting requirements and their influence on other requirements. A case study is also used to illustrate the application of this approach
6717527
An Efficient and Scalable Algorithm for Multi-Relational Frequent Pattern Discovery
We propose MRFPDA, an efficient and scalable algorithm for multi-relational frequent pattern discovery. We incorporate in the algorithm an optimal refinement operator to provide an improvement of the efficiency of candidate generation. Furthermore, MRFPDA utilizes a new strategy of sharing computations to avoid redundant computations in the candidate evaluation. In our experiments, it is shown that on small datasets the performance of MRFPDA is comparable with the performance of the state-of-the-art of multi-relational frequent pattern discovery, and on large datasets MRFPDA is more scalable than two existing approaches