Search results for " Computer Science"
showing 10 items of 3983 documents
A Problem Structuring Method
1991
Given a formal definition of problem and a formal definition of system, the equivalence between both concepts is studied. Considering a problem as a 3-tuple , where D is the set of possible data, R is the set of possible results, and P the set of conditions of the problem, classes of problems are constructed as combinations of types of data, types of results and types of conditions. For example, data can be either literal or numerical, either with uncertainty or not; conditions can be determined by rules, tables, equations, it may have uncertainty, etc. As a case of application it is outlined how some of the most common problems (knowledge representation, search, reasoning and planning, etc…
Geometric and conceptual knowledge representation within a generative model of visual perception
1989
A representation scheme of knowledge at both the geometric and conceptual levels is offered which extends a generative theory of visual perception. According to this theory, the perception process proceeds through different scene representations at various levels of abstraction. The geometric domain is modeled following the CSG (constructive solid geometry) approach, taking advantage of the geometric modelling scheme proposed by A. Pentland, based on superquadrics as representation primitives. Recursive Boolean combinations and deformations are considered in order to enlarge the scope of the representation scheme and to allow for the construction of real-world scenes. In the conceptual doma…
Some models of inductive syntactical synthesis from sample computations
2005
The paper is a survey of several models of inductive program synthesis from sample computations. Synthesis tools are basically syntactical: the synthesis is based on the detection of "regular" fragments related with "shuffled" arithmetical progressions. Input sample computations are supposed to be "representative": they have to "reflect" all loops occurring in the target program. Programs are synthesized in nontraditional form of "generalized" regular expressions having Cleene stars and unions for loops and CASE-like operators. However, if input samples are somehow "annotated" (we consider two different approaches), then loops can be synthesized in more traditional WHILE-form, where loop co…
On Utilizing Stochastic Non-linear Fractional Bin Packing to Resolve Distributed Web Crawling
2014
This paper deals with the extremely pertinent problem of web crawling, which is far from trivial considering the magnitude and all-pervasive nature of the World-Wide Web. While numerous AI tools can be used to deal with this task, in this paper we map the problem onto the combinatorially-hard stochastic non-linear fractional knapsack problem, which, in turn, is then solved using Learning Automata (LA). Such LA-based solutions have been recently shown to outperform previous state-of-the-art approaches to resource allocation in Web monitoring. However, the ever growing deployment of distributed systems raises the need for solutions that cope with a distributed setting. In this paper, we prese…
User Grouping and Power Allocation in NOMA Systems: A Reinforcement Learning-Based Solution
2020
In this paper, we present a pioneering solution to the problem of user grouping and power allocation in Non-Orthogonal Multiple Access (NOMA) systems. There are two fundamentally salient and difficult issues associated with NOMA systems. The first involves the task of grouping users together into the pre-specified time slots. The subsequent second phase augments this with the solution of determining how much power should be allocated to the respective users. We resolve this with the first reported Reinforcement Learning (RL)-based solution, which attempts to solve the partitioning phase of this issue. In particular, we invoke the Object Migration Automata (OMA) and one of its variants to re…
Combining finite learning automata with GSAT for the satisfiability problem
2010
A large number of problems that occur in knowledge-representation, learning, very large scale integration technology (VLSI-design), and other areas of artificial intelligence, are essentially satisfiability problems. The satisfiability problem refers to the task of finding a satisfying assignment that makes a Boolean expression evaluate to True. The growing need for more efficient and scalable algorithms has led to the development of a large number of SAT solvers. This paper reports the first approach that combines finite learning automata with the greedy satisfiability algorithm (GSAT). In brief, we introduce a new algorithm that integrates finite learning automata and traditional GSAT use…
Optimizing channel selection for cognitive radio networks using a distributed Bayesian learning automata-based approach
2015
Consider a multi-channel Cognitive Radio Network (CRN) with multiple Primary Users (PUs), and multiple Secondary Users (SUs) competing for access to the channels. In this scenario, it is essential for SUs to avoid collision among one another while maintaining efficient usage of the available transmission opportunities. We investigate two channel access schemes. In the first model, an SU selects a channel and sends a packet directly without Carrier Sensing (CS) whenever the PU is absent on this channel. In the second model, an SU invokes CS in order to avoid collision among co-channel SUs. For each model, we analyze the channel selection problem and prove that it is a so-called "Exact Potent…
Solving Graph Coloring Problems Using Learning Automata
2008
The graph coloring problem (GCP) is a widely studied combinatorial optimization problem with numerous applications, including time tabling, frequency assignment, and register allocation. The growing need for more efficient algorithms has led to the development of several GCP solvers. In this paper, we introduce the first GCP solver that is based on Learning Automata (LA). We enhance traditional Random Walk with LA-based learning capability, encoding the GCP as a Boolean satisfiability problem (SAT). Extensive experiments demonstrate that the LA significantly improve the performance of RW, thus laying the foundation for novel LA-based solutions to the GCP.
Learning multiresolution schemes for compression of images
2007
We introduce a new type of multiresolution based on the Harten's framework using learning theory. This changes the point of view of the classical multiresolution analysis and it transforms an approximation problem in a learning problem opening great possibilities. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
On t-Conorm Based Fuzzy (Pseudo)metrics
2020
We present an alternative approach to the concept of a fuzzy (pseudo)metric using t-conorms instead of t-norms and call them t-conorm based fuzzy (pseudo)metrics or just CB-fuzzy (pseudo)metrics. We develop the basics of the theory of CB-fuzzy (pseudo)metrics and compare them with “classic” fuzzy (pseudo)metrics. A method for construction CB-fuzzy (pseudo)metrics from ordinary metrics is elaborated and topology induced by CB-fuzzy (pseudo)metrics is studied. We establish interrelations between CB-fuzzy metrics and modulars, and in the process of this study, a particular role of Hamacher t-(co)norm in the theory of (CB)-fuzzy metrics is revealed. Finally, an intuitionistic version of a CB-fu…