38,879 research outputs found
The VEX-93 environment as a hybrid tool for developing knowledge systems with different problem solving techniques
The paper describes VEX-93 as a hybrid environment for developing
knowledge-based and problem solver systems. It integrates methods and
techniques from artificial intelligence, image and signal processing and
data analysis, which can be mixed. Two hierarchical levels of reasoning
contains an intelligent toolbox with one upper strategic inference engine
and four lower ones containing specific reasoning models: truth-functional
(rule-based), probabilistic (causal networks), fuzzy (rule-based) and
case-based (frames). There are image/signal processing-analysis capabilities
in the form of programming languages with more than one hundred primitive
functions.
User-made programs are embeddable within knowledge basis, allowing the
combination of perception and reasoning. The data analyzer toolbox contains
a collection of numerical classification, pattern recognition and ordination
methods, with neural network tools and a data base query language at
inference engines's disposal.
VEX-93 is an open system able to communicate with external computer programs
relevant to a particular application. Metaknowledge can be used for
elaborate conclusions, and man-machine interaction includes, besides windows
and graphical interfaces, acceptance of voice commands and production of
speech output.
The system was conceived for real-world applications in general domains, but
an example of a concrete medical diagnostic support system at present under
completion as a cuban-spanish project is mentioned.
Present version of VEX-93 is a huge system composed by about one and half
millions of lines of C code and runs in microcomputers under Windows 3.1.Postprint (published version
Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system
A number of representation schemes have been presented for use within
learning classifier systems, ranging from binary encodings to neural networks.
This paper presents results from an investigation into using discrete and fuzzy
dynamical system representations within the XCSF learning classifier system. In
particular, asynchronous random Boolean networks are used to represent the
traditional condition-action production system rules in the discrete case and
asynchronous fuzzy logic networks in the continuous-valued case. It is shown
possible to use self-adaptive, open-ended evolution to design an ensemble of
such dynamical systems within XCSF to solve a number of well-known test
problems
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An intelligent system for risk classification of stock investment projects
The proposed paper demonstrates that a hybrid fuzzy neural network can serve as a risk classifier of stock investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is compared with other crisp and soft investment appraisal and trading techniques, while building a multimodel domain representation for an intelligent decision support system. Thus the advantages of each model are utilised while looking at the investment problem from different perspectives. The empirical results are based on UK companies traded on the London Stock Exchange
On the Recognition of Fuzzy Circular Interval Graphs
Fuzzy circular interval graphs are a generalization of proper circular arc
graphs and have been recently introduced by Chudnovsky and Seymour as a
fundamental subclass of claw-free graphs. In this paper, we provide a
polynomial-time algorithm for recognizing such graphs, and more importantly for
building a suitable representation.Comment: 12 pages, 2 figure
TAIP: an anytime algorithm for allocating student teams to internship programs
In scenarios that require teamwork, we usually have at hand a variety of
specific tasks, for which we need to form a team in order to carry out each
one. Here we target the problem of matching teams with tasks within the context
of education, and specifically in the context of forming teams of students and
allocating them to internship programs. First we provide a formalization of the
Team Allocation for Internship Programs Problem, and show the computational
hardness of solving it optimally. Thereafter, we propose TAIP, a heuristic
algorithm that generates an initial team allocation which later on attempts to
improve in an iterative process. Moreover, we conduct a systematic evaluation
to show that TAIP reaches optimality, and outperforms CPLEX in terms of time.Comment: 10 pages, 7 figure
The Semantic Web Paradigm for a Real-Time Agent Control (Part II)
This paper is the second part of The Semantic Web Paradigm for a Real-time Agent Control, and the goal is to present the predictability of a multiagent system used in a learning process for a control problem (MASLCP).learning process, fuzzy control, agent predictability
A taxonomy for emergency service station location problem
The emergency service station (ESS) location problem has been widely
studied in the literature since 1970s. There has been a growing interest in the subject especially after 1990s. Various models with different objective functions and constraints have been proposed in the academic literature and efficient solution techniques have been developed to provide good solutions in reasonable times. However, there is not any study that systematically classifies different problem types and methodologies to address them. This paper presents a taxonomic framework for the ESS location problem using an operations research perspective. In this framework, we basically
consider the type of the emergency, the objective function, constraints, model
assumptions, modeling, and solution techniques. We also analyze a variety of papers related to the literature in order to demonstrate the effectiveness of the taxonomy and to get insights for possible research directions
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