BDI agents for Real Time Strategy games

While the use of distributed intelligence has been incrementally spreading in the design of a great number of intelligent systems, the field of Artificial Intelligence in Real Time Strategy games has remained mostly a centralized environment. Despite turn-based games have attained AIs of world-class level, the fast paced nature of RTS games has proven to be a significant obstacle to the quality of its AIs. Chapter 1 introduces RTS games describing their characteristics, mechanics and elements. Chapter 2 introduces Multi-Agent Systems and the use of the Beliefs-Desires-Intentions abstraction, analysing the possibilities given by self-computing properties. In Chapter 3 the current state of AI development in RTS games is analyzed highlighting the struggles of the gaming industry to produce valuable. The focus on improving multiplayer experience has impacted gravely on the quality of the AIs thus leaving them with serious flaws that impair their ability to challenge and entertain players. Chapter 4 explores different aspects of AI development for RTS, evaluating the potential strengths and weaknesses of an agent-based approach and analysing which aspects can benefit the most against centralized AIs. Chapter 5 describes a generic agent-based framework for RTS games where every game entity becomes an agent, each of which having its own knowledge and set of goals. Different aspects of the game, like economy, exploration and warfare are also analysed, and some agent-based solutions are outlined. The possible exploitation of self-computing properties to efficiently organize the agents activity is then inspected. Chapter 6 presents the design and implementation of an AI for an existing Open Source game in beta development stage: 0 a.d., an historical RTS game on ancient warfare which features a modern graphical engine and evolved mechanics. The entities in the conceptual framework are implemented in a new agent-based platform seamlessly nested inside the existing game engine, called ABot, widely described in Chapters 7, 8 and 9. Chapter 10 and 11 include the design and realization of a new agent based language useful for defining behavioural modules for the agents in ABot, paving the way for a wider spectrum of contributors. Chapter 12 concludes the work analysing the outcome of tests meant to evaluate strategies, realism and pure performance, finally drawing conclusions and future works in Chapter 13.

Airport Security: Passenger's Perception and Design Issues

The main goal of this thesis is to report patterns of perceived safety in the context of airport infrastructure, taking the airport of Bologna as reference. Many personal and environmental attributes are investigated to paint the profile of the sensitive passenger and to understand why precise factors of the transit environment are so impactful on the individual. The main analyses are based on a 2014-2015 passengers’ survey, involving almost six thousand of incoming and outgoing passengers. Other reports are used to implement and support the resource. The analysis is carried out by using a combination of Chi-square tests and binary logistic regressions. Findings shows that passengers result to be particularly affected by the perception of airport’s environment (e.g., state and maintenance of facilities, clarity and efficacy of information system, functionality of elevators and escalators), but also by the way how the passenger reaches the airport and the quality of security checks. In relation to such results, several suggestions are provided for the improvement of passenger satisfaction with safety. The attention is then focused on security checkpoints and related operations, described on a theoretical and technical ground. We present an example of how to realize a proper model of the security checks area of Bologna’s airport, with the aim to assess present performances of the system and consequences of potential variations. After a brief introduction to Arena, a widespread simulation software, the existing model is described, pointing out flaws and limitations. Such model is finally updated and changed in order to make it more reliable and more representative of the reality. Different scenarios are tested and results are compared using graphs and tables.