Multi-agent collaborative search : an agent-based memetic multi-objective optimization algorithm applied to space trajectory design

This article presents an algorithm for multi-objective optimization that blends together a number of heuristics. A population of agents combines heuristics that aim at exploring the search space both globally and in a neighbourhood of each agent. These heuristics are complemented with a combination of a local and global archive. The novel agent-based algorithm is tested at first on a set of standard problems and then on three specific problems in space trajectory design. Its performance is compared against a number of state-of-the-art multi-objective optimization algorithms that use the Pareto dominance as selection criterion: non-dominated sorting genetic algorithm (NSGA-II), Pareto archived evolution strategy (PAES), multiple objective particle swarm optimization (MOPSO), and multiple trajectory search (MTS). The results demonstrate that the agent-based search can identify parts of the Pareto set that the other algorithms were not able to capture. Furthermore, convergence is statistically better although the variance of the results is in some cases higher

Validation of machine-oriented strategies in chess endgames

This thesis is concerned with the validation of chess endgame strategies. It is also concerned with the synthesis of strategies that can be validated. A strategy for a given player is the specification of the move to be made by that player from any position that may occur. This move may be dependent on the previous moves of both sides. A strategy is said to be correct if following the strategy always leads to an outcome of at least the same game theoretic value as the starting position. We are not concerned with proving the correctness of programs that implement the strategies under consideration. We shall be working with knowledge-based programs which produce playing strategies, and assume that their concrete implementations (in POP2, PROLOG etc.) are correct. The synthesis approach taken attempts to use the large body of heuristic knowledge and theory, accumulated over the centuries by chessmasters, to find playing strategies. Our concern here is to produce structures for representing a chessmaster's knowledge wnich can be analysed within a game theoretic model. The validation approach taken is that a theory of the domain in the form of the game theoretic model of chess provides an objective measure of the strategy followed by a program. Our concern here is to analyse the structures created in the synthesis phase. This is an instance of a general problem, that of quantifying the performance of computing systems. In general to quantify the performance of a system we need,- A theory of the domain. - A specification of the problem to be solved. - Algorithms and/or domain-specific knowledge to be applied to solve the problem

Development of a Mobile Game to Influence Behavior Determinants of HIV Service Uptake Among Key Populations in the Philippines: User-Centered Design Process

Opportunities in digital distribution place mobile games as a promising platform for games for health. However, designing a game that can compete in the saturated mobile games market and deliver persuasive health messages can feel like an insurmountable challenge. Although user-centered design is widely advocated, factors such as the user's subject domain expertise, budget constraints, and poor data collection methods can restrict the benefits of user involvement. OBJECTIVE: This study aimed to develop a playable and acceptable game for health, targeted at young key populations in the Philippines. METHODS: Authors identified a range of user-centered design methods to be used in tandem from published literature. The resulting design process involved a phased approach, with 40 primary and secondary users engaged during the initial ideation and prototype testing stages. Selected methods included participatory design workshops, playtests, playability heuristics, and focus group discussions. Subject domain experts were allocated roles in the development team. Data were analyzed using a framework approach. Conceptual frameworks in health intervention acceptability and game design guided the analysis. In-game events were captured through the Unity Analytics service to monitor uptake and game use over a 12-month period. RESULTS: Early user involvement revealed a strong desire for online multiplayer gameplay, yet most reported that access to this type of game was restricted because of technical and economic constraints. A role-playing game (RPG) with combat elements was identified as a very appealing gameplay style. Findings guided us to a game that could be played offline and that blended RPG elements, such as narrative and turn-based combat, with match-3 puzzles. Although the game received a positive response during playtests, gameplay was at times perceived as repetitive and predicted to only appeal to casual gamers. Knowledge transfer was predominantly achieved through interpretation of the game's narrative, highlighting this as an important design element. Uptake of the game was positive; between December 1, 2017, and December 1, 2018, 3325 unique device installs were reported globally. Game metrics provided evidence of adoption by young key populations in the Philippines. Game uptake and use were substantially higher in regions where direct engagement with target users took place. CONCLUSIONS: User-centered design activities supported the identification of important contextual requirements. Multiple data collection methods enabled triangulation of findings to mediate the inherent biases of the different techniques. Game acceptance is dependent on the ability of the development team to implement design solutions that address the needs and desires of target users. If target users are expected to develop design solutions, they must have adequate expertise and a significant role within the development team. Facilitating meaningful partnerships between health professionals, the games industry, and end users will support the games for health industry as it matures

On Teaching Professional Judgment

To answer the question posed by the conveners of this symposium, of course there is a gap between legal education and the legal profession. There has always been one, and quite possibly it has widened somewhat in recent years, if for no other reason than that the world in which lawyers practice has changed so much while legal education has changed relatively little. The external changes include the internationalization of legal transactions, the centrality of technology to many aspects of practice, increased specialization driven by the proliferation and complexity of statutory and regulatory schemes, and the overloading of traditional systems of civil and criminal justice. Perhaps more significant than any of these is the unhappy fact that today\u27s law school graduates will enter a society that views them with hostility and suspicion and regards their impact on our national culture and economy as often more negative than positiv

A heuristic-based approach to code-smell detection

Encapsulation and data hiding are central tenets of the object oriented paradigm. Deciding what data and behaviour to form into a class and where to draw the line between its public and private details can make the difference between a class that is an understandable, flexible and reusable abstraction and one which is not. This decision is a difficult one and may easily result in poor encapsulation which can then have serious implications for a number of system qualities. It is often hard to identify such encapsulation problems within large software systems until they cause a maintenance problem (which is usually too late) and attempting to perform such analysis manually can also be tedious and error prone. Two of the common encapsulation problems that can arise as a consequence of this decomposition process are data classes and god classes. Typically, these two problems occur together – data classes are lacking in functionality that has typically been sucked into an over-complicated and domineering god class. This paper describes the architecture of a tool which automatically detects data and god classes that has been developed as a plug-in for the Eclipse IDE. The technique has been evaluated in a controlled study on two large open source systems which compare the tool results to similar work by Marinescu, who employs a metrics-based approach to detecting such features. The study provides some valuable insights into the strengths and weaknesses of the two approache

Reasoning in criminal intelligence analysis through an argumentation theory-based framework

This thesis provides an in-depth analysis of criminal intelligence analysts’ analytical reasoning process and offers an argumentation theory-based framework as a means to support that reasoning process in software applications. Researchers have extensively researched specific areas of criminal intelligence analysts’ sensemaking and reasoning processes over the decades. However, the research is fractured across different research studies and those research studies often have high-level descriptions of how criminal intelligence analysts formulate their rationale (argument). This thesis addresses this gap by offering low level descriptions on how the reasoning-formulation process takes place. It is presented as a single framework, with supporting templates, to inform the software implementation process. Knowledge from nine experienced criminal intelligence analysts from West Midlands Police and Belgium’s Local and Federal Police forces were elicited through a semi-structured interview for study 1 and the Critical Decision Method (CDM), as part of the Cognitive Task Analysis (CTA) approach, was used for study 2 and study 3. The data analysis for study 1 made use of the Qualitative Conventional Content Analysis approach. The data analysis for study 2 made use of a mixed method approach, consisting out of Qualitative Directed Content Analysis and the Emerging Theme Approach. The data analysis for study 3 made use of the Qualitative Directed Content Analysis approach. The results from the three studies along with the concepts from the existing literature informed the construction of the argumentation theory-based framework. The evaluation study for the framework’s components made use of Paper Prototype Testing as a participatory design method over an electronic medium. The low-fidelity prototype was constructed by turning the frameworks’ components into software widgets that resembled widgets on a software application’s toolbar. Eight experienced criminal intelligence analysts from West Midlands Police and Belgium’s Local and Federal Police forces took part in the evaluation study. Participants had to construct their rationale using the available components as part of a simulated robbery crime scenario, which used real anonymised crime data from West Midlands Police force. The evaluation study made use of a Likert scale questionnaire to capture the participant’s views on how the frameworks’ components aided participants with; understanding what was going on in the analysis, lines-of-enquiry and; the changes in their level of confidence pertaining to their rationale. A non-parametric, one sample z-test was used for reporting the statistical results. The significance is at 5% (α=0.05) against a median of 3 for the z-test, where μ =3 represents neutral. The participants reported a positive experience with the framework’s components and results show that the framework’s components aided them with formulating their rationale and understanding how confident they were during different phases of constructing their rationale

Explaining and Refining Decision-Theoretic Choices

As the need to make complex choices among competing alternative actions is ubiquitous, the reasoning machinery of many intelligent systems will include an explicit model for making choices. Decision analysis is particularly useful for modelling such choices, and its potential use in intelligent systems motivates the construction of facilities for automatically explaining decision-theoretic choices and for helping users to incrementally refine the knowledge underlying them. The proposed thesis addresses the problem of providing such facilities. Specifically, we propose the construction of a domain-independent facility called UTIL, for explaining and refining a restricted but widely applicable decision-theoretic model called the additive multi-attribute value model. In this proposal we motivate the task, address the related issues, and present preliminary solutions in the context of examples from the domain of intelligent process control

Low-resource learning in complex games

This project is concerned with learning to take decisions in complex domains, in games in particular. Previous work assumes that massive data resources are available for training, but aside from a few very popular games, this is generally not the case, and the state of the art in such circumstances is to rely extensively on hand-crafted heuristics. On the other hand, human players are able to quickly learn from only a handful of examples, exploiting specific characteristics of the learning problem to accelerate their learning process. Designing algorithms that function in a similar way is an open area of research and has many applications in today’s complex decision problems. One solution presented in this work is design learning algorithms that exploit the inherent structure of the game. Specifically, we take into account how the action space can be clustered into sets called types and exploit this characteristic to improve planning at decision time. Action types can also be leveraged to extract high-level strategies from a sparse corpus of human play, and this generates more realistic trajectories during planning, further improving performance. Another approach that proved successful is using an accurate model of the environment to reduce the complexity of the learning problem. Similar to how human players have an internal model of the world that allows them to focus on the relevant parts of the problem, we decouple learning to win from learning the rules of the game, thereby making supervised learning more data efficient. Finally, in order to handle partial observability that is usually encountered in complex games, we propose an extension to Monte Carlo Tree Search that plans in the Belief Markov Decision Process. We found that this algorithm doesn’t outperform the state of the art models on our chosen domain. Our error analysis indicates that the method struggles to handle the high uncertainty of the conditions required for the game to end. Furthermore, our relaxed belief model can cause rollouts in the belief space to be inaccurate, especially in complex games. We assess the proposed methods in an agent playing the highly complex board game Settlers of Catan. Building on previous research, our strongest agent combines planning at decision time with prior knowledge extracted from an available corpus of general human play; but unlike this prior work, our human corpus consists of only 60 games, as opposed to many thousands. Our agent defeats the current state of the art agent by a large margin, showing that the proposed modifications aid in exploiting general human play in highly complex games