Maxim Mozgovoy

Efficient parameterized string matching

In parameterized string matching the pattern P matches a substring t of the text T if there exist a bijective mapping from the symbols of P to the symbols of t. We give simple and practical algorithms for finding all such pattern occurrences in sublinear time on average. The algorithms work for a single and multiple patterns.

Fast plagiarism detection system

The large class sizes typical for an undergraduate programming course mean that it is nearly impossible for a human marker to accurately detect plagiarism, particularly if some attempt has been made to hide the copying. While it would be desirable to be able to detect all possible code transformations we believe that there is a minimum level of acceptable performance for the application of detecting student plagiarism. It would be useful if the detector operated at a level that meant for a piece of work to fool the algorithm would require that the student spent a large amount of time on the assignment and had a good enough understanding to do the work without plagiarising.

Automatic student plagiarism detection : future perspectives

The availability and use of computers in teaching has seen an increase in the rate of plagiarism among students because of the wide availability of electronic texts online. While computer tools that have appeared in recent years are capable of detecting simple forms of plagiarism, such as copy-paste, a number of recent research studies devoted to evaluation and comparison of plagiarism detection tools revealed that these contain limitations in detecting complex forms of plagiarism such as extensive paraphrasing and use of technical tricks, such as replacing original characters with similar-looking characters from foreign alphabets. This article investigates limitations in automatic detection of student plagiarism and proposes ways on how these issues could be tackled in future systems by applying various natural language processing and information retrieval technologies. A classification of types of plagiarism is presented, and an analysis is provided of the most promising technologies that have the potential of dealing with the limitations of current state-of-the-art systems. Furthermore, the article concludes with a discussion on legal and ethical issues related to the use of plagiarism detection software. The article, hence, provides a “roadmap” for developing the next generation of plagiarism detection systems.

Hermetic and Web Plagiarism Detection Systems for Student Essays--An Evaluation of the State-of-the-Art.

Plagiarism has become a serious problem in education, and several plagiarism detection systems have been developed for dealing with this problem. This study provides an empirical evaluation of eight plagiarism detection systems for student essays. We present a categorical hierarchy of the most common types of plagiarism that are encountered in student texts. Our purpose-built test set contains texts in which instances of several commonly utilized plagiaristic techniques have been embedded. While Sherlock was clearly the overall best hermetic detection system, SafeAssignment performed best in detecting web plagiarism. TurnitIn was found to be the most advanced system for detecting semi-automatic forms of plagiarism such as the substitution of Cyrillic equivalents for certain characters or the insertion of fake whitespaces. The survey indicates that none of the systems are capable of reliably detecting plagiarism from both local and Internet sources while at the same time being able to identify the technical tricks that plagiarizers use to conceal plagiarism.

Believable and Effective AI Agents in Virtual Worlds: Current State and Future Perspectives

The rapid development of complex virtual worlds (most notably, in 3D computer and video games) introduces new challenges for the creation of virtual agents, controlled by artificial intelligence (AI) systems. Two important subproblems in this topic area which need to be addressed are (a) believability and (b) effectiveness of agents’ behavior, i.e. human-likeness of the characters and high ability to achieving their own goals. In this paper, we study current approaches to believability and effectiveness of AI behavior in virtual worlds. We examine the concepts of believability and effectiveness, and analyze several successful attempts to address these challenges. In conclusion, we suggest that believable and effective behavior can be achieved through learning behavioral patterns from observation with subsequent automatic selection of winning acting strategies.

Fast and reliable plagiarism detection system

Plagiarism and similarity detection software is well-known in universities for years. Despite the variety of methods and approaches used in plagiarism detection, the typical trade-off between the speed and the reliability of the algorithm still remains. We introduce a new two-step approach to plagiarism detection that combines high algorithmic performance and the quality of pairwise file comparison. Our system uses fast detection method to select suspicious files only, and then invokes precise (and slower) algorithms to get reliable results. We show that the proposed method does not noticeably reduce the quality of the pairwise comparison mechanism while providing better speed characteristics.

WordBricks: a virtual language lab inspired by Scratch environment and dependency grammars

This paper explains design decisions forming a foundation of WordBricks — an intelligent computer-assisted language learning environment, recently initiated at our institution. WordBricks is intended to serve as a “virtual language lab” that supports open experiments with natural language constructions. Being based on dependency grammars, this instrument illustrates the use of modern natural language processing technologies in language learning. The latest prototypes of WordBricks also show how dependency-styled constructions can be represented in a more natural sequential form that facilitates easier user interaction.

Computer simulation of stick balancing: action point analysis

We analyze data collected during the series of experiments aimed at elucidation of basic properties of human perception, namely, the limited capacity of ordering events, actions, etc. according to their preference. Previously it was shown that in a wide class of human-controlled systems small deviations from the equilibrium position do not cause any actions of the systems operator, so any point in a certain neighborhood of equilibrium position is treated as an equilibrium one. This phenomenon can be described by the notion of dynamical traps that was introduced to denote a region in the system phase space where the object under consideration cannot clearly determine the most preferable of the positions that are similar in some sense. According to this concept, the motion of the system in the dynamical trap region is mainly not affected by the operator. The moments of time when the system leaves the dynamical trap region, or in other words, when the operator decides to start or stop the control over the system, are called action points [1]. These moments are seem to be determined intuitively by the operator, and the purpose of our work is to understand the nature of such intuitive decision making process by investigating the action points data obtained from the experiments.

Building a believable and effective agent for a 3D boxing simulation game

This paper describes an approach used to build and optimize a practical AI solution for a 3D boxing simulation game. The two main features of the designed AI agent are believability (human-likeness of agents behavior) and effectiveness (agents capability to reach own goals). We show how learning by observation and case-based reasoning techniques are used to create believable behavior. Then we employ reinforcement learning to optimize agents behavior, turning the agent into a strong opponent, acting in a commercial-level game environment. The used knowledge representation scheme supports high maintainability, important for game developers.

Building a Believable Agent for a 3D Boxing Simulation Game

This paper describes an approach used to build a practical AI solution for a 3D boxing simulation game. The features of the designed AI agent are based on our deliberate concentration on believability, i.e. human-likeness of agent’s behavior. We show how learning by observation and case-based reasoning techniques can be used to create an AI decision-making system for an industrial-level computer game. The chosen AI design principles support high usability and maintainability, which is important for game developers. We prove experimentally that our AI system provides both believable and effective behavior.