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- articleMay 2017
Efficient Particle Swarm Optimized Particle Filter Based Improved Multiple Model Tracking Algorithm
Computational Intelligence (COMI), Volume 33, Issue 2May 2017, Pages 262–279https://doi.org/10.1111/coin.12084To meet the requirements of modern radar maneuvering target tracking system and remedy the defects of interacting multiple model based on particle filter, noninteracting multiple model NIMM and enhanced particle swarm optimized particle filter EPSO-PF ...
- articleFebruary 2017
Evolutionary Algorithm-Based Radial Basis Function Neural Network Training for Industrial Personal Computer Sales Forecasting
Computational Intelligence (COMI), Volume 33, Issue 1February 2017, Pages 56–76https://doi.org/10.1111/coin.12073Forecasting is one of the crucial factors in applications because it ensures the effective allocation of capacity and proper amount of inventory. Because Box-Jenkins models using linear forecasting have their constraint to predict complexity in the real ...
- articleNovember 2016
A Multiagent Evolutionary Method for Detecting Communities in Complex Networks
Computational Intelligence (COMI), Volume 32, Issue 4November 2016, Pages 587–614https://doi.org/10.1111/coin.12067Community structure detection in complex networks contributes greatly to the understanding of complex mechanisms in many fields. In this article, we propose a multiagent evolutionary method for discovering communities in a complex network. The focus of ...
- articleMay 2016
Creating Decision Trees from Rules using RBDT-1
Computational Intelligence (COMI), Volume 32, Issue 2May 2016, Pages 216–239https://doi.org/10.1111/coin.12049Most of the methods that generate decision trees for a specific problem use the examples of data instances in the decision tree-generation process. This article proposes a method called RBDT-1-rule-based decision tree-for learning a decision tree from a ...
- articleNovember 2015
Building a Language-Independent Discourse Parser using Universal Networking Language
Computational Intelligence (COMI), Volume 31, Issue 4November 2015, Pages 593–618https://doi.org/10.1111/coin.12037Discourse parsing has become an inevitable task to process information in the natural language processing arena. Parsing complex discourse structures beyond the sentence level is a significant challenge. This article proposes a discourse parser that ...
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- articleAugust 2015
Training Multiagent Systems by Q-Learning: Approaches and Empirical Results
Computational Intelligence (COMI), Volume 31, Issue 3August 2015, Pages 498–512https://doi.org/10.1111/coin.12035Multiagent systems are increasingly present in computational environments. However, the problem of agent design or control is an open research field. Reinforcement learning approaches offer solutions that allow autonomous learning with minimal ...
- articleAugust 2015
An Infrastructure for Argumentative Agents
Computational Intelligence (COMI), Volume 31, Issue 3August 2015, Pages 418–441https://doi.org/10.1111/coin.12030Multiagent systems are suitable for providing a framework that allows agents to perform collaborative processes in a social context. Furthermore, argumentation is a natural way of reaching agreements between several parties. However, it is difficult to ...
- articleMay 2015
Undirected Dependency Parsing
Computational Intelligence (COMI), Volume 31, Issue 2May 2015, Pages 348–384https://doi.org/10.1111/coin.12027Dependency parsers, which are widely used in natural language processing tasks, employ a representation of syntax in which the structure of sentences is expressed in the form of directed links dependencies between their words. In this article, we ...
- articleMay 2015
Attitude Sensing in Text Based on A Compositional Linguistic Approach
Computational Intelligence (COMI), Volume 31, Issue 2May 2015, Pages 256–300https://doi.org/10.1111/coin.12020In this article, we introduce a compositional linguistic approach for attitude recognition in text. There are several aspects that distinguish our attitude analysis model @AM from other systems. First, our method classifies sentences using fine-grained ...
- articleFebruary 2015
A Computational Framework for Practical Social Reasoning
Computational Intelligence (COMI), Volume 31, Issue 1February 2015, Pages 69–105https://doi.org/10.1111/coin.12014This article describes a framework for practical social reasoning designed to be used for analysis, specification, and implementation of the social layer of agent reasoning in multiagent systems. Our framework, called the expectation strategy behavior ...
- articleNovember 2014
A GENERIC TRUST FRAMEWORK FOR LARGE-SCALE OPEN SYSTEMS USING MACHINE LEARNING
Computational Intelligence (COMI), Volume 30, Issue 4November 2014, Pages 700–721https://doi.org/10.1111/coin.12022In many large-scale distributed systems and on the Web, agents need to interact with other unknown agents to carry out some tasks or transactions. The ability to reason about and assess the potential risks in carrying out such transactions is essential ...
- articleMay 2014
DESIGNING PROTOCOLS FOR ABDUCTIVE HYPOTHESIS REFINEMENT IN DYNAMIC MULTIAGENT ENVIRONMENTS
Computational Intelligence (COMI), Volume 30, Issue 2May 2014, Pages 362–401https://doi.org/10.1111/j.1467-8640.2012.00468.xThis paper studies multiagent systems where each agent has access to local observations of a dynamic environment and needs to build from this partial information an hypothesis on the state of the system. Each agent ensures that its hypothesis is ...
- articleMay 2014
ELIMINATING CONCEPTS AND ROLES FROM ONTOLOGIES IN EXPRESSIVE DESCRIPTIVE LOGICS
Computational Intelligence (COMI), Volume 30, Issue 2May 2014, Pages 205–232https://doi.org/10.1111/j.1467-8640.2012.00442.xForgetting is an important tool for reducing ontologies by eliminating some redundant concepts and roles while preserving sound and complete reasoning. Attempts have previously been made to address the problem of forgetting in relatively simple ...
- articleFebruary 2014
MAINTENANCE GOALS IN INTELLIGENT AGENTS
Computational Intelligence (COMI), Volume 30, Issue 1February 2014, Pages 71–114https://doi.org/10.1111/coin.12000Intelligent agent systems are often used to implement complex software systems. A key aspect of agent systems is goals: a programmer or user defines a set of goals for an agent, and then the agent is left to determine how best to satisfy the goals ...
- articleNovember 2012
Declarative Specification Of Fault Tolerant Auction Protocols: The English Auction Case Study
Computational Intelligence (COMI), Volume 28, Issue 4November 2012, Pages 617–641https://doi.org/10.1111/j.1467-8640.2012.00448.xAuction mechanisms are nowadays widely used in electronic commerce Web sites for buying and selling items among different users. The increasing importance of auction protocols in the negotiation phase is not limited to online marketplaces. In fact, the ...
- articleNovember 2012
Dempster's Rule As Seen By Little Colored Balls
Computational Intelligence (COMI), Volume 28, Issue 4November 2012, Pages 453–474https://doi.org/10.1111/j.1467-8640.2012.00421.xDempster’s rule is traditionally interpreted as an operator for fusing belief functions. While there are different types of belief fusion, there has been considerable confusion regarding the exact type of operation that Dempster’s rule performs. Many ...
- articleAugust 2012
EXPLOITING SUBTREES IN AUTO-PARSED DATA TO IMPROVE DEPENDENCY PARSING
Computational Intelligence (COMI), Volume 28, Issue 3August 2012, Pages 426–451https://doi.org/10.1111/j.1467-8640.2012.00451.xDependency parsing has attracted considerable interest from researchers and developers in natural language processing. However, to obtain a high-accuracy dependency parser, supervised techniques require a large volume of hand-annotated data, which are ...
- articleMay 2012
Evolutionary Shallow Natural Language Parsing
Computational Intelligence (COMI), Volume 28, Issue 2May 2012, Pages 156–175https://doi.org/10.1111/j.1467-8640.2012.00412.xIdentifying syntactical information from natural-language texts requires the use of sophisticated parsing techniques mainly based on statistical and machine-learning methods. However, due to complexity and efficiency issues many intensive natural-...