A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

Intelligent Agents for Active Malware Analysis

The main contribution of this thesis is to give a novel perspective on Active Malware Analysis modeled as a decision making process between intelligent agents. We propose solutions aimed at extracting the behaviors of malware agents with advanced Artificial Intelligence techniques. In particular, we devise novel action selection strategies for the analyzer agents that allow to analyze malware by selecting sequences of triggering actions aimed at maximizing the information acquired. The goal is to create informative models representing the behaviors of the malware agents observed while interacting with them during the analysis process. Such models can then be used to effectively compare a malware against others and to correctly identify the malware famil

Intelligent group movement and selection in realtime strategy games

Movement of groups in realtime strategy games is often a nuisance: Units travel and battle separately, resulting in huge losses and the AI looking dumb. This applies to computer as well as human commanded factions. We suggest to tackle that by using flocking improved by influence-map based pathfinding which leads to a much more natural and intelligent looking behavior. A similar problem occurs if the computer AI has to select groups to combat a specific target: Assignment of units to groups, especially for multiple enemy groups, is often suboptimal when units have very different attack skills. This can be cured by using offline prepared self-organizing feature maps that use all available information for looking up good matches. We demonstrate that these two approaches work well separately, but also that they go together very naturally, thereby leading to an improved and - via parametrization - very flexible group behavior. Opponent AI may be strenghtened that way as well as player-supportive AI. A thorough experimental analysis supports our claims

Universal Algorithm for Creating A Small Scale Reusable Simulation Data in Real-time Strategy Games

Real-time strategy games are of such high complexity that consideration of trying to brute force all actions and states is not only impractical, but impossible. Approximations, information abstractions, and models are, therefore, the necessity when creating game bots that play this genre of games. To create such bots, the detailed data is needed to base them on. This article introduces a universal algorithm that creates reusable simulation data of one attacking unit on a building and tests the feasibility of doing such a task. This paper concludes that capturing all relevant data in a sub-segment of real-time strategygames is feasible. Gathered data holds valuable information and can be reused in new research without the need of repeating the simulations

Designing a training tool for imaging mental models

The training process can be conceptualized as the student acquiring an evolutionary sequence of classification-problem solving mental models. For example a physician learns (1) classification systems for patient symptoms, diagnostic procedures, diseases, and therapeutic interventions and (2) interrelationships among these classifications (e.g., how to use diagnostic procedures to collect data about a patient's symptoms in order to identify the disease so that therapeutic measures can be taken. This project developed functional specifications for a computer-based tool, Mental Link, that allows the evaluative imaging of such mental models. The fundamental design approach underlying this representational medium is traversal of virtual cognition space. Typically intangible cognitive entities and links among them are visible as a three-dimensional web that represents a knowledge structure. The tool has a high degree of flexibility and customizability to allow extension to other types of uses, such a front-end to an intelligent tutoring system, knowledge base, hypermedia system, or semantic network

Spartan Daily September 21, 2010

Volume 135, Issue 12https://scholarworks.sjsu.edu/spartandaily/1175/thumbnail.jp

Spartan Daily September 21, 2010

Volume 135, Issue 12https://scholarworks.sjsu.edu/spartandaily/1175/thumbnail.jp

Alternative Modes for Teaching Mathematical Problem Solving: An Overview

Various modes are proffered as alternatives for teaching mathematical problem solving. Each mode is described briefly, along with general purposes, advantages and disadvantages. Combinations of modes are suggested; general issues identified; recommendations offered; and feedback from teachers summarized