76909
Heuristic search in artificial intelligence
In February of this year, Claude E. Shannon and Herbert A. Simon passed away, both at age 84. In his 1950 paper entitled, “Programming a Computer to Play Chess” [3], Shannon laid out the theory of minimax search with bounded lookahead and static heuristic evaluation, the basis of almost all subsequent work on two-player games. Along with Allen Newell, Herbert Simon established the idea of heuristic search in a problem space as one of the fundamental paradigms of problem solving in artificial intelligence [1]. We feel a deep sorrow for the loss of these two giants of our field. Both men are also known for their deep scientific contributions to other fields as well. Shannon was the father of information and communication theory, and Simon was a founding father of organizational behavior and the information processing model of cognitive psychology. Although Simon and Shannon are no longer with us, their work continues as a source of inspiration in virtually every field they touched. We dedicate this special issue on heuristic search in artificial intelligence to Claude Shannon and Herbert Simon. Heuristic search remains as a core area of artificial intelligence (AI). The use of a good search algorithm is often a critical factor in the performance of an intelligent system. As with most areas of AI, there has been steady progress in heuristic search research over the years. This progress can be measured by several different yardsticks, including finding optimal solutions to larger problems, making higher quality decisions in fixedsize problems, handling more complex domains including dynamic environments with incomplete and uncertain information, being able to analyze and predict the performance of heuristic search algorithms, and the increasing deployment of real-world applications of search algorithms. The success of the Deep Blue Chess machine in defeating Gary Kasparov, the human world champion, in an exhibition match in 1997 was a landmark in the field of AI, the achievement of a goal that had been pursued for almost 50 years. By coincidence, this special issue contains the same number of papers as the Artificial Intelligence Journal special issue on Search and Heuristics, published in 1983. What Judea
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.
1196346
An Efficient Hybrid Strategy for Temporal Planning
Temporal planning (TP) is notoriously difficult because it requires to solve a propositional STRIPS planning problem with temporal constraints. In this paper, we propose an efficient strategy for solving TP, which combines, in an innovative way, several well established and studied techniques in AI, OR and constraint programming. Our approach integrates graph planning (a well studied planning paradigm), max-SAT (a constraint optimization technique), and the Program Evaluation and Review Technique (PERT), a well established technique in OR. Our method first separates the logical and temporal constraints of a TP problem and solves it in two phases. In the first phase, we apply our new STRIPS planner to generate a parallel STRIPS plan with a minimum number of parallel steps. Our new STRIPS planner is based on a new max-SAT formulation, which leads to an effective incremental learning scheme and a goal-oriented variable selection heuristic. The new STRIPS planner can generate optimal parallel plans more efficiently than the well-known SATPLAN approach. In the second phase, we apply PERT to schedule the activities in a parallel plan to create a shortest temporal plan given the STRIPS plan. When applied to the first optimal parallel STRIPS plan, this simple strategy produces optimal temporal plans on most benchmarks we have tested. This strategy can also be applied to optimal STRIPS plans of different parallel steps in an anytime fashion to find an optimal temporal plan. Our experimental results show that the new strategy is effective and the resulting algorithm outperforms many existing temporal planners.
10379128
Searching for backbones and fat: a limit-crossing approach with applications
Backbone variables are the elements that are common to all optimal solutions of a problem instance. We call variables that are absent from every optimal solution <i>fat</i> variables. Identification of backbone and fat variables is a valuable asset when attempting to solve complex problems. In this paper, we demonstrate a method for identifying backbones and fat. Our method is based on an intuitive concept, which we refer to as <i>limit crossing</i>. Limit crossing occurs when we force the lower bound of a graph problem to exceed the upper bound by applying the lower-bound function to a constrained version of the graph. A desirable feature of this procedure is that it uses approximation functions to derive exact information about optimal solutions. In this paper, we prove the validity of the limit-crossing concept as well as other related properties. Then we exploit limit crossing and devise a pre-processing tool for discovering backbone and fat arcs for various instances of the Asymmetric Traveling Salesman Problem (ATSP). Our experimental results demonstrate the power of the limit-crossing method. We compare our pre-processor with the Carpaneto, Dell'Amico, and Toth pre-processor for several different classes of ATSP instances and reveal dramatic performance improvements.
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.
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.
28880809
A feature-oriented approach to modeling requirements dependencies
There are many researches on requirements dependencies. However, most of them limit their views to the requirements phase of software development, few focus on the roles of requirements dependencies in the solution space of software. This paper presents a feature-oriented approach to modeling requirements dependencies. A feature is a set of tight-related requirements from user/customer-views. The feature-orientation provides a modular way to organize requirements and a proper granularity to analyze requirements dependencies. In this approach, we care about not only static feature dependencies (i. e. refinements and constraints), but also feature dependencies at the specification level (namely influences) and, furthermore, dynamic feature dependencies (namely interactions). Moreover, we also explore the underlying connections between these four kinds of feature dependency. By this way, this approach gives a more complete view of how requirements dependencies influence the whole process of software development.
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.
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
34728545
Data dissemination with ring-based index for wireless sensor networks
In current sensor networks, sensor nodes are capable of not only measuring real world phenomena, but also storing, processing and transferring these measurements. Many data dissemination techniques have been proposed for sensor networks. However, these techniques may not work well in a large scale sensor network where a huge amount of sensing data are generated, but only a small portion of them are queried. In this paper, we propose an index-based data dissemination scheme to address the problem. This scheme is based on the idea that sensing data are collected, processed and stored at the nodes close to the detecting nodes, and the location information of these storing nodes is pushed to some index nodes, which act as the rendezvous points for sinks and sources. We further extend the scheme with an adaptive ring-based index (ARI) technique, in which the index nodes for one event type form a ring surrounding the location which is determined by the event type, and the ring can be dynamically reconfigured for fault tolerance and load balance. Analysis and simulations are conducted to evaluate the performance of the proposed index-based scheme. The results show that the index-based scheme outperforms the external storage-based scheme, the DCS scheme, and the local storage-based schemes with flood-response style. The results also show that using ARI can tolerate clustering failures and achieve load balance.