7030953
Energy-aware partitioning for multiprocessor real-time systems
In this paper, we address the problem of partitioning periodic real-time tasks in a multiprocessor platform by considering both feasibility and energy-awareness perspectives: our objective is to compute the feasible partitioning that results in minimum energy consumption on multiple identical processors by using variable voltage earliest-deadline-first scheduling. We show that the problem is NP-hard in the strong sense on m /spl ges/ 2 processors even when feasibility is guaranteed a priori. Then, we develop our framework where load balancing plays a major role in producing energy-efficient partitionings. We evaluate the feasibility and energy-efficiency performances of partitioning heuristics experimentally.
215965918
A Parallel Scheme Using the Divide-and-Conquer Method
A parallel scheme using the divide-and-conquer method is developed. This partitions the input set of a problem into subsets, computes a partial result from each subset, and finally employs a mergin...
42167729
Computation of chain queries in distributed database systems
7531376
Deriving a Stationary Dynamic Bayesian Network from a Logic Program with Recursive Loops
Recursive loops in a logic program present a challenging problem to the PLP framework. On the one hand, they loop forever so that the PLP backward-chaining inferences would never stop. On the other hand, they generate cyclic influences, which are disallowed in Bayesian networks. Therefore, in existing PLP approaches logic programs with recursive loops are considered to be problematic and thus are excluded. In this paper, we propose an approach that makes use of recursive loops to build a stationary dynamic Bayesian network. Our work stems from an observation that recursive loops in a logic program imply a time sequence and thus can be used to model a stationary dynamic Bayesian network without using explicit time parameters. We introduce a Bayesian knowledge base with logic clauses of the form A ← A1,...,Al, true, Context, Types, which naturally represents the knowledge that the Ais have direct influences on A in the context Context under the type constraints Types. We then use the well-founded model of a logic program to define the direct influence relation and apply SLG-resolution to compute the space of random variables together with their parental connections. We introduce a novel notion of influence clauses, based on which a declarative semantics for a Bayesian knowledge base is established and algorithms for building a two-slice dynamic Bayesian network from a logic program are developed.
4541399
Discriminant analysis with tensor representation
In this paper, we present a novel approach to solving the supervised dimensionality reduction problem by encoding an image object as a general tensor of 2nd or higher order. First, we propose a discriminant tensor criterion (DTC), whereby multiple interrelated lower-dimensional discriminative subspaces are derived for feature selection. Then, a novel approach called k-mode cluster-based discriminant analysis is presented to iteratively learn these subspaces by unfolding the tensor along different tensor dimensions. We call this algorithm discriminant analysis with tensor representation (DATER), which has the following characteristics: 1) multiple interrelated subspaces can collaborate to discriminate different classes; 2) for classification problems involving higher-order tensors, the DATER algorithm can avoid the curse of dimensionality dilemma and overcome the small sample size problem; and 3) the computational cost in the learning stage is reduced to a large extent owing to the reduced data dimensions in generalized eigenvalue decomposition. We provide extensive experiments by encoding face images as 2nd or 3rd order tensors to demonstrate that the proposed DATER algorithm based on higher order tensors has the potential to outperform the traditional subspace learning algorithms, especially in the small sample size cases.
16808814
Feature Weight Maintenance in Case Bases Using Introspective Learning
A key issue in case-based reasoning is how to maintain the domain knowledge in the face of a changing environment. During the case retrieval process in case-based reasoning, feature-value pairs are used to compute the ranking scores of the cases in a case base, and different feature-value pairs may have different importance measures, represented as weight values, in this computation. How to maintain a set of appropriate feature weights so that they can be used to solve future problems effectively and efficiently will be a key factor in determining the success of case-based reasoning applications.Our focus in this paper is on the dynamic maintenance of feature weights in a case base. We address a particular problem related to the feature-weight maintenance issue. In current practice, the feature weights are assigned and revised manually, not only making them highly informal and inaccurate, but also involving intensive labor. We would like to introduce a semi-automatic introspective learning method to partially address this issue. Our approach is to construct a network architecture on the case base that supports introspective learning. Weight learning and weight-evolution are accomplished in the background through the integration of a learning network into case-based reasoning, in which, while the reasoning part is still case based, the learning part is shouldered by a layered network. The computation in the network follows well-known neural network algorithms with well known properties. We demonstrate the effectiveness of our approach through experiments.
39839249
A Theory of Conflict Resolution in Planning
Abstract Conflict resolution in planning is the process of constraining a plan to remove harmful interactions that threaten its correctness. It has been a major contributing factor to the complexity of classical planning systems. Traditional planning methods have dealt with the problem of conflict resolution in a local and incremental manner, by considering and resolving conflicts individually. This paper presents a theory of conflict resolution that supports a global consideration of conflicts. The theory enables one to formally represent, reason about and resolve conflicts using an extended framework of constraint satisfaction. The computational advantage of the theory stems from its ability to remove inconsistencies early in a search process, to detect dead ends with low computational overhead, to remove redundancies in a search space, and to guide the search by providing an intelligent order in which to resolve conflicts. The paper also presents empirical results showing the utilities of the theory, by investigating the characteristics of problem domains where the theory is expected to work well, and the types of planning systems for which the theory can offer a marked computational advantage.
2850409
A Framework for Automatic Problem Decomposition in Planning
An intelligent problem solver must be able to decompose a complex problem into simpler parts. A decomposition algorithm would not only be beneficial for traditional subgoal-oriented planning systems but also support distributed, multi-agent planners. In this paper, we present an algorithm for automatic problem decomposition. Given a domain description with a number of objects to be manipulated, our method constructs subspaces complete with subproblem descriptions and operators, and solves the subproblems concurrently. The solutions in individual subspaces are combined using a constraint satisfaction algorithm. The effectiveness of the approach is guaranteed by our careful analysis of the interactions among different subspaces. The results presented in this paper support parallel, distributed and multi-agent planning systems.