Eliska Ochodkova

Robot Soccer - Strategy Description And Game Analysis.

The robot soccer game, as a part of standard applications of distributed system control in real time, provides numerous opportunities for the application of AI. Real-time dynamic strategy description and strategy learning possibility based on game observation are important to discover opponent’s strategies, search tactical group movements and synthesize proper counter-strategies. In this paper, the game is separated into physical part and logical part including strategy level and abstract level. Correspondingly, the game strategy description and prediction of ball motion are built up. The way to use this description, such as learning rules and adapting team strategies to every single opponent, is also discussed. Cluster analysis is used to validate the strategy extraction.

Searching for quasigroups for hash functions with genetic algorithms

In this study we discuss a method for evolution of quasigroups with desired properties based on genetic algorithms. Quasigroups are a well-known combinatorial design equivalent to the more familiar Latin squares. One of their most important properties is that all possible elements of certain quasigroup occur with equal probability. The quasigroups are evolved within a framework of a simple hash function. Prior implementations of quasigroups were based on look-up table of the quasigroup, which is infeasible for large quasigroups. In contrast, analytic quasigroup can be implemented easily. It allows the evaluation of hash function without storing large amount of data (look-up table) and the concept of homotopy enables consideration of many quasigropus.

Local Community Detection and Visualization: Experiment Based on Student Data

This paper is focused on the detection of communities in social networks. We propose and describe a novel method for detecting local communities. We have used this method in an experiment on student social networks in order to prove our hypothesis about the nature of student communities. The results of the experiment rationalized our hypothesis and confirmed the effectiveness of the described method of local community detection.

Genetic algorithms evolving quasigroups with good pseudorandom properties

Quasigroups are a well-known combinatorial design equivalent to more familiar Latin squares. Because all possible elements of a quasigroup occur with equal probability, it makes it an interesting tool for the application in computer security and for production of pseudorandom sequences. Prior implementations of quasigroups were based on look-up table of the quasigroup, on system of distinct representatives etc. Such representations are infeasible for large quasigroups. In contrast, presented analytic quasigroup can be implemented easily. It allows the generation of pseudorandom sequences without storing large amount of data (look-up table). The concept of isotopy enables consideration of many quasigroups and genetic algorithms allow efficient search for good ones.

Testing the properties of large quasigroups

With the growing importance of data security a growing effort to find new approaches to the cryptographic algorithms designs appears. One of the trends is to research the use of other algebraic structures than the traditional, such as a quasigroup. Quasigroups are equivalent to the more familiar Latin squares. There are many characteristics that must quasigroups have from the cryptography point of view. They have to be non-commutative, non-associative, non-idempotent and so on. If one want to work with quasigroups of a large order, effective methods of testing their properties are necessary. In this paper we present several experiments on various types of guasigroups and their results.

Quasigroups with good statistical properties

Quasigroups are a well-known combinatorial design equivalent to more familiar Latin squares. Since all possible elements of a quasigroup occur with equal probability, it could be an interesting tool for information assurance and network security. Most of the previous implementations of quasigroups were based on look-up table of the quasigroup, on system of distinct representatives etc. which is infeasible for large quasigroups. This paper presents statistical tests which enable us selection of good quasigroups.

A New Iterative Approach for Finding Nearest Neighbors Using Space-Filling Curves for Fast Graphs Visualization

Graphs in the computer science are widely used in social network analysis, computer networks, transportation networks, and many other areas. In general, they can visualize relationships between objects. However, fast drawing of graphs with readable layouts is still a challenge. This paper aims to the speed up the original Fruchterman-Reingold graph layout algorithm by computing repulsive forces only between vertices that are near each other. A new approach based on the selected space-filling curves is described.

Genetic search for quasigroups with heterogeneous power sequences

Genetic algorithms have been successfully used for searching for quasigroups with good properties. In this study we extend previous work done on evolutionary search for quasigroups by defining new fitness functions based on selected algebraical properties of the quasigroups. Introduced fitness functions make use of heterogeneous sequences generated during the exponentiation of quasigroups elements. Effects of the artificial evolution utilizing proposed fitness functions are illustrated on quasigroups of the order 8, 10, and 12.

Loop Strategies and Application of Rough Set Theory in Robot Soccer Game

The robot soccer game is full of challenging in the field of robot and artificial intelligence. Strategy is a kernel subsystem of the game. According to the strategy description in our work, there exists a problem of loop strategies. We present the concept of condition-decision relation matrix by which the loop strategies can be found. Together with rough set theory, we illustrate the creation process of consistent decision table and validate our method by an experiment of loop strategies detection.

Graph Construction Based on Local Representativeness.

Graph construction is a known method of transferring the problem of classic vector data mining to network analysis. The advantage of networks is that the data are extended by links between certain (similar) pairs of data objects, so relationships in the data can then be visualized in a natural way. In this area, there are many algorithms, often with significantly different results. A common problem for all algorithms is to find relationships in data so as to preserve the characteristics related to the internal structure of the data. We present a method of graph construction based on a network reduction algorithm, which is found on analysis of the representativeness of the nodes of the network. It was verified experimentally that this algorithm preserves structural characteristics of the network during the reduction. This approach serves as the basis for our method which does not require any default parameters. In our experiments, we show the comparison of our graph construction method with one well-known method based on the most commonly used approach.