Showing papers in "Journal of Advanced Computational Intelligence and Intelligent Informatics in 2007"

Fuzzy Concept Lattices Constrained by Hedges

Abstract: We study concept lattices constrained by hedges. The principal aim is to control, in a parameterical way, the size of concept lattices, i.e. the number of conceptual clusters extracted from data. The paper presents theoretical insight, comments, and examples. We introduce new, parameterized, concept-forming operators and study their properties. We obtain an axiomatic characterization of the concept-forming operators. Then, we show that a concept lattice with hedges is indeed a complete lattice which is isomorphic to an ordinary concept lattice. We describe the isomorphism and its inverse. These mappings serve as translation procedures. As a consequence, we obtain a theorem characterizing the structure of concept lattices with hedges which generalizes the well-known main theorem of ordinary concept lattices. Furthermore, the isomorphism and its inverse enable us to compute a concept lattice with hedges using algorithms for ordinary concept lattices. Further insight is provided for boundary choices of hedges. We demonstrate by experiments that the size reduction using hedges as parameters is smooth.

Robust Non-Overshoot Time Responses using Cascade Sliding Mode-PID Control

Abstract: Overshoot is a serio us problem in automatic control systems. This paper presents a new method for elimination of the step response overshoot in a conventional PID-controlled system and enhancement of its robustness by cascading a sliding mode controller in the outer loop. The idea is first to use the cascade control principle to model the under-damped system under PID control with a second-order system. Then, by making use of the sliding mode control outer loop, a robust, reduced-order response can be obtained to suppress the control overshoot. The proposed approach can also deal with time delay systems. Its v alidity is verified through simulation for some dynamic systems subject to hig hly nonlinear uncertainties, where overshoot remains an issue.

Genetic and bacterial programming for B-Spline neural networks design

Abstract: The design phase of B-spline neural networks is a highly computationally complex task. Existent heuristics have been found to be highly dependent on the initial conditions employed. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this paper, the Bacterial Programming approach is presented, which is based on the replication of the microbial evolution phenomenon. This technique produces an efficient topology search, obtaining additionally more consistent solutions.

Human Head Tracking Based on Particle Swarm Optimization and Genetic Algorithm

Abstract: This paper compares particle swarm optimization and a genetic algorithm for perception by a partner robot The robot requires visual perception to interact with human beings It should basically extract moving objects using visual perception in interaction with human beings To reduce computational cost and time consumption, we used differential extraction We propose human head tracking for a partner robot using particle swarm optimization and a genetic algorithm Experiments involving two maximum iteration numbers show that particle swarm optimization is more effective in solving this problem than genetic algorithm

Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming with Ant Colony Optimization with Evaporation

Abstract: Recently, Artificial Intelligence (AI) technology has been applied to many applications. As an extension of Genetic Algorithm (GA) and Genetic Programming (GP), Genetic Network Programming (GNP) has been proposed, whose gene is constructed by directed graphs. GNP can perform a global searching, but its evolving speed is not so high and its optimal solution is hard to obtain in some cases because of the lack of the exploitation ability of it. To alleviate this difficulty, we developed a hybrid algorithm that combines Genetic Network Programming (GNP) with Ant Colony Optimization (ACO) with Evaporation. Our goal is to introduce more exploitation mechanism into GNP. In this paper, we applied the proposed hybrid algorithm to a complicated real world problem, that is, Elevator Group Supervisory Control System (EGSCS). The simulation results showed the effectiveness of the proposed algorithm.

Embodied conversational agents for H5N1 pandemic crisis

Abstract: This paper presents a novel framework for modeling embodied conversational agent for crisis communication focusing on the H5N1 pandemic crisis. Our system aims to cope with the most challenging issue on the maintenance of an engaging while convincing conversation. What primarily distinguishes our system from other conversational agent systems is that the human-computer conversation takes place within the context of H5N1 pandemic crisis. A Crisis Communication Network, called CCNet, is established based on a novel algorithm incorporating natural language query and embodied conversation agent simultaneously. Another significant contribution of our work is the development of a Automated Knowledge Extraction Agent (AKEA) to capitalize on the tremendous amount of data that is now available online to support our experiments. What makes our system differs from typical conversational agents is the attempt to move away from strictly task-oriented dialogue.

Anthropomorphization Framework for Human-Object Communication

Abstract: We propose an anthropomorphization framework that determines an object’s body image. This framework directly intervenes and anthropomorphizes objects in ubiquitous-computing environments through robotic body parts shaped like those of human beings, which provide information through spoken directions and body language. Our purpose is to demonstrate that an object acquires subjective representations through anthropomorphization. Using this framework, people can more fully understand instructions given by an object. We designed an anthropomorphization framework that changes the body image by attaching body parts. We also conducted experiments to evaluate this framework. Results indicate that the site at which an anthropomorphization device is attached influences human perception of the object’s virtual body image, and participants in experiments understood several instructions given by the object more clearly. Results also indicate that participants better intuited their devices’ instructions and movement in ubiquitous-computing environments.

Coincidence-Based Scoring of Mappings in Ontology Alignment

Abstract: Ontology Matching (OM) which targets finding a set of alignments across two ontologies, is a key enabler for the success of Semantic Web. In this paper, we introduce a new perspective on this problem. By interpreting ontologies as Typed Graphs embedded in a Metric Space, coincidence of the structures of the two ontologies is formulated. Having such a formulation, we define a mechanism to score mappings. This scoring can then be used to extract a good alignment among a number of candidates. To do this, this paper introduces three approaches: The first one, straightforward and capable of finding the optimum alignment, investigates all possible alignments, but its runtime complexity limits its use to small ontologies only. To overcome this shortcoming, we introduce a second solution as well which employs a Genetic Algorithm (GA) and shows a good effectiveness for some certain test collections. Based on approximative approaches, a third solution is also provided which, for the same purpose, measures random walks in each ontology versus the other.

Dynamically Adjusting Migration Rates for Multi-Population Genetic Algorithms

Abstract: In this paper, the issue of adapting migration parameters for MGAs is investigated. We examine, in particular, the effect of adapting the migration rates on the performance and solution quality of MGAs. Thereby, we propose an adaptive scheme to adjust the appropriate migration rates for MGAs. If the individuals from a neighboring sub-population can greatly improve the solution quality of a current population, then the migration from the neighbor has a positive effect. In this case, the migration rate from the neighbor should be increased; otherwise, it should be decreased. According to the principle, an adaptive multi-population genetic algorithm which can adjust the migration rates is proposed. Experiments on the 0/1 knapsack problem are conducted to show the effectiveness of our approach. The results of our work have illustrated the effectiveness of self-adaptation for MGAs and paved the way for this unexplored area.

Adaptive Action Selection of Body Expansion Behavior in Multi-Robot System Using Communication

Abstract: In multi-robot system, cooperation is needed to execute tasks efficiently. The purpose of this study is to realize cooperation among multiple robots using interactive communication. An important role of communication in multi-robot system is to make it possible to control other robots by intention transmission. We consider that multi-robot system can be more and more adaptive by treating communication as action. In this report, we adopt action adjustment function to achieve cooperation between robots. We also run some computer simulations of collision avoidance as an example of cooperative task, and discuss the results.