Catálogo de publicaciones - libros

DNA-based memories have the potential to store vast amount of information with high density. In this paper, a new DNA-based semantic model is proposed and described theoretically for DNA-based memories. This model, referred to as ‘’ (SMC) has the structure of a graph formed by the set of all attribute-attribute value pairs contained in the set of represented objects, plus a tag node for each object. Each path in the network, from an initial object-representing tag node to a terminal node represents the object named on the tag. Object-representing circular dsDNAs will be formed via parallel self-assembly, from encoded ssDNAs representing attribute-attribute value pairs, as directed by ssDNA splinting strands representing relations in the network. The proposed semantic models are rather suitable for DNA-based memories.

Binary factor analysis (BFA) is a nonhierarchical binary data analysis, based on reduction of binary space dimension. It allows us to find hidden relationships in binary data, which can be used for efficient data compression, data mining, or intelligent data comparison for information retrieval. It seems that genetic algorithm (GA) may be used to find the solution. This paper describes two GA variants usable for BFA. The better one is described in detail, and results of some experiments are shown, comparing it with other known BFA methods. The experiments reveal that the new method based on revised genetic algorithm performs very well.

Technostress is the general term of the malfunction condition which treating the information management system accompanied by VDTs (Visual Display Terminals). The applied technology of ubiquitous computing will enhance the affinity between a computer and person. However, continuous use of VDTs sometimes causes technostress to us. The purpose of this study was to establish the method of reducing the visual fatigue during VDT work. In order to obtain a better reasoning result, it is necessary to deal with environmental information, VDT information and user information such as both psychological and physiological information, comprehensively. We, then, represent the causal association of visual fatigue using Bayesian network. Result showed the causal relationship between visual fatigue and VDT work factors visually.

Biochemical reaction theory based DNA computation is of much better performance in solving a class of intractable computational problems such as NP-complete problems, it is important to study the DNA computation. A novel algorithm based on DNA computation is proposed, which solves the 0–1 planning problem finally by using the surface-based fluorescence labeling technique.

In this paper, we define the Fixed-Radius Fixed-Diameter model and have the probability of the graph being connected graph in one-dimensional space. Furthermore, by using that result, we also obtain the probability of the Fixed-Radius Free-Diameter model being connected graph in one-dimensional space.

As future works, we would like to analyze the connectivity of our model when the number of nodes is sufficiency large and the model of which the radius is variable. Furthermore, we would like to analyze the connectivity of the two-dimensional random graph model based on our results.

Software agent technology is currently based on fixed ontologies and languages, hand-crafted for a particular application. The advent of massively distributed systems however calls for not only a common language between all agents involved but also the ability to autonomously adapt and form concepts about novel experiences and events. We propose a method by which agents can autonomously form new concepts grounded in their own experience. This is an improvement on previous approaches because it can tackle a much wider range of conceptual types and provides an efficient, accurate representation which can be used in a rule based system. Furthermore, our method allows an agent to simultaneously learn new concepts and the rules to govern its behaviour whilst providing a more robust system which adapts well to both new and changeable environments.

The partitioning of sets or graphs is an exhaustively researched topic in classic and modern computer science. We consider this area from a new point of view by developing algorithms for partitioning with only very limited abilities and knowledge of the individual elements, restricted to the direct, local environment of the single elements (locality). In our approach the elements or vertices of the graph are represented by agents and the edges illustrate communication lines between two agents. Several partitioning strategies will be presented and compared with each other and we will explain the most promising one for multiple target partitioning in a detailed way.

The main objective of the proposed multi-agent infrastructure is to help practitioners and users alike navigate and coordinate solutions offered from different applications to solve complex problems. In order to obtain effective solutions support the above objective, we investigate the coupling of sensor techniques with traditional agent interfaces within the modern e-business environment. In such an environment, different types of applications already exist. We describe in this paper how the proposed infrastructure offers an alternative solution for applications integration.

The models of adaptive consensus formation are useful for obtaining harmonious consensus of a group when all members of the group pursue their individual rationality. In the process of consensus formation, the agents access to macro information and adapt their preferences to the overall group preference. In this paper, we propose and analyze some quantitative measures of the difference between the individual preferences and the agreed preference. Moreover, we introduce a model of the agents with the different speeds of adaption and analyze its effects to consensus formation by means of simulation.

We have developed a system (SimSoccer Coach) that shows single agent learning by analyzing the fixed opponent’s behavior and then providing offensive and defensive advice to improve the team’s performance. For the offensive advice, the system learns through imitation of successful passing and shooting actions of the opponent’s previous games. For defensive advice, the system learns through observation of the opponent’s passing behavior and thwarts any passing attempts by marking the player and intercepting the ball. To generate these sets of advice, the system reads logfiles of previous games played by a fixed opponent against other teams and selects the data to be used for learning. The C4.5 decision tree algorithm is used to construct the tree and generate production rules based on the selected data. These production rules are converted into CLang advice following the Coach Language grammar. These CLang rules are then given to the coachable team before the game.