NPC AI System Based on Gameplay Recordings

Hästi optimeeritud mitte-mängija tegelased (MMT) on vastaste või meeskonna kaaslastena üheks peamiseks osaks mitme mängija mängudes. Enamus mänguroboteid on ehitatud jäikade süsteemide peal, mis võimaldavad vaid loetud arvu otsuseid ja animatsioone. Kogenud mängijad suudavad eristada mänguroboteid inimmängijatest ning ette ennustada nende liigutusi ja strateegiaid. See alandab mängukogemuse kvaliteeti. Seetõttu, eelistavad mitme mängijaga mängude mängijad mängida pigem inimmängijate kui MMTde vastu. Virtuaalreaalsuse (VR) mängud ja VR mängijad on siiani veel väike osa mängutööstusest ja mitme mängija VR mängud kannatavad mängijabaasi kaotusest, kui mänguomanikud ei suuda leida teisi mängijaid, kellega mängida. See uurimus demonstreerib mängulindistustel põhineva tehisintellekt (TI) süsteemi rakendatavust VR esimese isiku vaates tulistamismängule Vrena. Teemamäng kasutab ebatavalist liikumisesüsteemi, milles mängijad liiguvad otsiankrute abil. VR mängijate liigutuste imiteerimiseks loodi AI süsteem, mis kasutab mängulindistusi navigeerimisandmetena. Süsteem koosneb kolmest peamisest funktsionaalsusest. Need funktsionaalsused on mängutegevuse lindistamine, andmete töötlemine ja navigeerimine. Mängu keskkond on tükeldatud kuubikujulisteks sektoriteks, et vähendada erinevate asukohal põhinevate olekute arvu ning mängutegevus on lindistatud ajaintervallide ja tegevuste põhjal. Loodud mängulogid on segmenteeritud logilõikudeks ning logilõikude abil on loodud otsingutabel. Otsingutabelit kasutatakse MMT agentide navigeerimiseks ning MMTde otsuste langetamise mehanism jäljendab olek-tegevus-tasu kontseptsiooni. Loodud töövahendi kvaliteeti hinnati uuringu põhjal, millest saadi märkimisväärset tagasisidet süsteemi täiustamiseks.A well optimized Non-Player Character (NPC) as an opponent or a teammate is a major part of the multiplayer games. Most of the game bots are built upon a rigid system with numbered decisions and animations. Experienced players can distinguish bots from hu-man players and they can predict bot movements and strategies. This reduces the quality of the gameplay experience. Therefore, multiplayer game players favour playing against human players rather than NPCs. VR game market and VR gamers are still a small frac-tion of the game industry and multiplayer VR games suffer from loss of their player base if the game owners cannot find other players to play with. This study demonstrates the applicability of an Artificial Intelligence (AI) system based on gameplay recordings for a Virtual Reality (VR) First-person Shooter (FPS) game called Vrena. The subject game has an uncommon way of movement, in which the players use grappling hooks to navigate. To imitate VR players’ movements and gestures an AI system is developed which uses gameplay recordings as navigation data. The system contains three major functionality. These functionalities are gameplay recording, data refinement, and navigation. The game environment is sliced into cubic sectors to reduce the number of positional states and gameplay is recorded by time intervals and actions. Produced game logs are segmented into log sections and these log sections are used for creating a look-up table. The lookup table is used for navigating the NPC agent and the decision mechanism followed a way similar to the state-action-reward concept. The success of the developed tool is tested via a survey, which provided substantial feedback for improving the system

Blast-Building Leaders for Advancing Science and Technology: A Partnership Between the Virginia Space Grant Consortium and the University of Virginia, Virginia Polytechnic Institute, and Old Dominion University

This paper presents the development and delivery of educational summer intensive programs for high school students that are designed to encourage students’ interests in the STEM-related fields and the motivation to pursue a STEM-related degrees in college. BLAST (Building Leaders to Advance Science and Technology) is designed as a summer-intensive, residential, on-campus STEM-learning experience for rising ninth and tenth graders. With the intention of improving the STEM-related workforce pipeline in the Commonwealth of Virginia, Virginia Space Grant Consortium (VSGC) offers multiple BLAST programs across the Commonwealth. BLAST programs are designed as intensive three-day, STEM-related three-hour lecture-lab experiences that are reinforced by evening STEM-related events. Funded by a grant by the National Aeronautics and Space Administration (NASA), VSGC targets approximately three hundred students annually who have a C+ or better average, and who have had no previous STEM-related experience. It is surmised that if more students are exposed to STEM-related fields, they may become more interested in and motivated to one-day pursue a STEM-related discipline which would help to alleviate the STEM-related workforce shortages in Virginia. BLAST is offered at three public universities in Virginia including the University of Virginia, Virginia Tech, and Old Dominion University. Faculty and graduate students at each of the respective universities design and implement programs that draw upon their respective faculty interests and strengths. In this paper, a content analysis of the various BLAST programs and interviews with the directors and faculty involved were conducted to identify common and unique strengths across the different BLAST programs. Impacts of COVID on the development and delivery of the BLAST programs are addressed, as are suggestions for program improvements. The purpose of this paper is to share the results of perceived impacts of the BLAST programs on increasing high school students\u27 interest in STEM-related fields and to increase their motivation in the pursuit of STEM-related college degrees. If the U.S. is to be successful at improving its STEM-ready workforce, one solution is to increase the number of high school students pursuing a STEM-related degree and career

Aesthetic Programming

Aesthetic Programming explores the technical as well as cultural imaginaries of programming from its insides. It follows the principle that the growing importance of software requires a new kind of cultural thinking — and curriculum — that can account for, and with which to better understand the politics and aesthetics of algorithmic procedures, data processing and abstraction. It takes a particular interest in power relations that are relatively under-acknowledged in technical subjects, concerning class and capitalism, gender and sexuality, as well as race and the legacies of colonialism. This is not only related to the politics of representation but also nonrepresentation: how power differentials are implicit in code in terms of binary logic, hierarchies, naming of the attributes, and how particular worldviews are reinforced and perpetuated through computation. Using p5.js, it introduces and demonstrates the reflexive practice of aesthetic programming, engaging with learning to program as a way to understand and question existing technological objects and paradigms, and to explore the potential for reprogramming wider eco-socio-technical systems. The book itself follows this approach, and is offered as a computational object open to modification and reversioning

Bringing computational thinking to K-12 and higher education

Doctor of PhilosophyDepartment of Computer ScienceWilliam H. HsuSince the introduction of new curriculum standards at K-12 schools, computational thinking has become a major research area. Creating and delivering content to enhance these skills, as well as evaluation, remain open problems. This work describes different interventions based on the Scratch programming language aimed toward improving student self-efficacy in computer science and computational thinking. These interventions were applied at a STEM outreach program for 5th-9th grade students. Previous experience in STEM-related activities and subjects, as well as student self-efficacy, were surveyed using a developed pre- and post-survey. The impact of these interventions on student performance and confidence, as well as the validity of the instrument are discussed. To complement attitude surveys, a translation of Scratch to Blockly is proposed. This will record student programming behaviors for quantitative analysis of computational thinking in support of student self-efficacy. Outreach work with Kansas Starbase, as well as the Girl Scouts of the USA, is also described and evaluated. A key goal for computational thinking in the past 10 years has been to bring computer science to other disciplines. To test the gap from computer science to STEM, computational thinking exercises were embedded in an electromagnetic fields course. Integrating computation into theory courses in physics has been a curricular need, yet there are many difficulties and obstacles to overcome in integrating with existing curricula and programs. Recommendations from this experimental study are given towards integrating CT into physics a reality. As part of a continuing collaboration with physics, a comprehensive system for automated extraction of assessment data for descriptive analytics and visualization is also described

The Montclarion, February 10, 2000

Student Newspaper of Montclair State Universityhttps://digitalcommons.montclair.edu/montclarion/1864/thumbnail.jp

Current, July 11, 2005

https://irl.umsl.edu/current2000s/1265/thumbnail.jp

Examining approaches to target validation and drug repurposing in large scale genomic projects.

PhD Theses Medical.Drug repurposing presents an opportunity to quickly produce new medications in a cost effective manner. This is especially important in rare diseases where patients are frequently underserved. Here, we apply various methods to first select good targets for repurposing. We analyse loss-of-function (LoF) data, and assess its role in informing drug discovery. We achieve this by curating, aggregating and labelling LoF data and then building a model to predict genes that may harbour homozygous LoF with no negative associated phenotypes. We produce a model with a relatively high degree of accuracy and recall (F-score 0.7), generating 442 predicted genes in addition to 1,744 from aggregation. Following this, we assess whether such data could inform drug discovery in collaboration with AbbVie, an industrial partner. Through the study of historic drug data, comparing our LoF labels with data from previous studies detailing the effect of genetic knowledge on drug discovery, and against the loss-of-function observed/expected upper bound fraction (LOEUF) score, a metric of constraint, we demonstrate that this data adds significant value to drug discovery. Finally, we build a database focussing on rare diseases, and use LoF data, in addition to drug data and expertly curated gene panels to nominate candidates for repurposing. This database will be made available for researchers within the GEL community, such that avenues for repurposing can be further explored

User Intention Modelling and Intention Recognition in Games using World State Indicators

The work presented in this thesis focuses on two main areas. First we develop a model of user intentions in games. That model defines various concepts related to player behaviour in (computer) games and interactive environments, such as actions, goals, plans and intentions. Additionally our model shows the relationship between those concepts and explains how they affect each other. The purpose of the model presented here is twofold. One the one hand it provides common definitions for research in the area of user behaviour modelling in games. On the other hand this model also forms the underlying basis for the remainder of our work presented here. The second main area of focus is intention recognition in games. We propose a novel approach which is solely based on monitoring the changes in the game world state, instead of observing player actions. We evaluate current approaches to plan and intention recognition, their strengths and weaknesses. We further compare existing research on intention recognition to our approach and evaluate the performance of our prototype system iRecognise in the context of a case study using the board game RISK. A range of experiments that were carried out demonstrates that our proposed approach to intention recognition is valid and therefore verifies its intention recognition capabilities in the context of games

Learning domain abstractions for long lived robots

Recent trends in robotics have seen more general purpose robots being deployed in unstructured environments for prolonged periods of time. Such robots are expected to adapt to different environmental conditions, and ultimately take on a broader range of responsibilities, the specifications of which may change online after the robot has been deployed. We propose that in order for a robot to be generally capable in an online sense when it encounters a range of unknown tasks, it must have the ability to continually learn from a lifetime of experience. Key to this is the ability to generalise from experiences and form representations which facilitate faster learning of new tasks, as well as the transfer of knowledge between different situations. However, experience cannot be managed na¨ıvely: one does not want constantly expanding tables of data, but instead continually refined abstractions of the data – much like humans seem to abstract and organise knowledge. If this agent is active in the same, or similar, classes of environments for a prolonged period of time, it is provided with the opportunity to build abstract representations in order to simplify the learning of future tasks. The domain is a common structure underlying large families of tasks, and exploiting this affords the agent the potential to not only minimise relearning from scratch, but over time to build better models of the environment. We propose to learn such regularities from the environment, and extract the commonalities between tasks. This thesis aims to address the major question: what are the domain invariances which should be learnt by a long lived agent which encounters a range of different tasks? This question can be decomposed into three dimensions for learning invariances, based on perception, action and interaction. We present novel algorithms for dealing with each of these three factors. Firstly, how does the agent learn to represent the structure of the world? We focus here on learning inter-object relationships from depth information as a concise representation of the structure of the domain. To this end we introduce contact point networks as a topological abstraction of a scene, and present an algorithm based on support vector machine decision boundaries for extracting these from three dimensional point clouds obtained from the agent’s experience of a domain. By reducing the specific geometry of an environment into general skeletons based on contact between different objects, we can autonomously learn predicates describing spatial relationships. Secondly, how does the agent learn to acquire general domain knowledge? While the agent attempts new tasks, it requires a mechanism to control exploration, particularly when it has many courses of action available to it. To this end we draw on the fact that many local behaviours are common to different tasks. Identifying these amounts to learning “common sense” behavioural invariances across multiple tasks. This principle leads to our concept of action priors, which are defined as Dirichlet distributions over the action set of the agent. These are learnt from previous behaviours, and expressed as the prior probability of selecting each action in a state, and are used to guide the learning of novel tasks as an exploration policy within a reinforcement learning framework. Finally, how can the agent react online with sparse information? There are times when an agent is required to respond fast to some interactive setting, when it may have encountered similar tasks previously. To address this problem, we introduce the notion of types, being a latent class variable describing related problem instances. The agent is required to learn, identify and respond to these different types in online interactive scenarios. We then introduce Bayesian policy reuse as an algorithm that involves maintaining beliefs over the current task instance, updating these from sparse signals, and selecting and instantiating an optimal response from a behaviour library. This thesis therefore makes the following contributions. We provide the first algorithm for autonomously learning spatial relationships between objects from point cloud data. We then provide an algorithm for extracting action priors from a set of policies, and show that considerable gains in speed can be achieved in learning subsequent tasks over learning from scratch, particularly in reducing the initial losses associated with unguided exploration. Additionally, we demonstrate how these action priors allow for safe exploration, feature selection, and a method for analysing and advising other agents’ movement through a domain. Finally, we introduce Bayesian policy reuse which allows an agent to quickly draw on a library of policies and instantiate the correct one, enabling rapid online responses to adversarial conditions

Bypassing Modern CPU Protections With Function-Oriented Programming

Over the years, code reuse attacks such as return-oriented programming (ROP) and jump-oriented programming (JOP) have been a primary target to gain execution on a system via buffer overflow, memory corruption, and code flow hijacking vulnerabilities. However, new CPU-level protections have introduced a variety of hurdles. ARM has designed the “Pointer Authentication” and “Branch Target Identification” mechanisms to handle the authentication of memory addresses and pointers, and Intel has followed through with its Shadow Stack and Indirect Branch Targeting mechanisms, otherwise known as Control-Flow Enforcement Technology. As intended, these protections make it nearly impossible to utilize regular code reuse methods such as ROP and JOP. The inclusion of these new protections has left gaps in the system\u27s security where the use of function-based code reuse attacks are still possible. This research demonstrates a novel approach to utilizing Function-Oriented Programming (FOP) as a technique to utilize in such environments. The design and creation of the “FOP Mythoclast” tool to identify FOP gadgets within Intel and ARM environments demonstrates not only a proof of concept (PoC) for FOP, but further cements its ability to thrive in diverse constrained environments. Additionally, the demonstration of FOP within the Linux kernel showcases the ability of FOP to excel in complex and real-world situations. This research concludes with potential solutions for mitigating FOP without adversely affecting system performance