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.

Disruptive Marketing Strategy - ribaltare i classici modelli di approccio al mercato

Viviamo in un epoca di cambiamenti. Continuamente, giorno dopo giorno, il vecchio scompare per fare spazio al nuovo. La tecnologia permea sempre più in ogni aspetto della vita, conquistando le nostre abitudini, cambiando il nostro modo di vedere il mondo, e, in maniera inevitabile, il nostro modo di agire. L’essere umano ha da sempre cercato di concepire cose che ancora non ci sono, per cambiare la realtà e modellarla a suo piacimento. Si parte, quindi, da qualcosa che fin dalla nascita è dentro ognuno di noi, un desiderio che arde nell'animo umano, che si alimenta grazie al soffio dell’immaginazione e che non c’è modo di spegnere. L’innovazione porta novità. Resistere al cambiamento non è sempre facile, e se si cerca di ostacolarlo esso ti travolgerà. “Affrontare il cambiamento è come scalare una colata di fango giù per una collina” - esordisce in “The Innovator Dilemma”, Clayton M. Christensen - “Bisogna affrontare qualsiasi cosa per rimanere su di essa, e non appena ci si ferma per prendere fiato si viene seppelliti”. Si intuisce come il cambiamento tecnologico non solo sia di fondamentale evidenza nella vita di ognuno, ma come esso abbia cambiato e stia cambiando il modo di fare business. In questo umile lavoro si è voluto analizzare come certi tipi di innovazione abbiano il potere di cambiare tutto un sistema di riferimento sul quale prima l’impresa faceva affidamento. Sono le Disruptive Innovation – le innovazioni dirompenti – quelle innovazioni che ribaltano un modello di business, il modo di vedere un prodotto, la logica dietro a certe operations aziendali, che seppur sembrassero solide, si sgretolano scontrandosi con innovazioni così forti. Vi è poi una domanda critica a cui si è cercato di rispondere. Si può fare Disruptive Marketing? Alcuni esempi virtuosi ci mostrano come si possa attuare una strategia di questo genere, e come si possa “comprendere, creare, comunicare e distribuire valore” in modo innovativo.

Development of an innovative non rule-based energy management strategy for a Hybrid Electric Vehicle, structured for predictive driving controls based on external information knowledge

Recently, the interest of the automotive market for hybrid vehicles has increased due to the more restrictive pollutants emissions legislation and to the necessity of decreasing the fossil fuel consumption, since such solution allows a consistent improvement of the vehicle global efficiency. The term hybridization regards the energy flow in the powertrain of a vehicle: a standard vehicle has, usually, only one energy source and one energy tank; instead, a hybrid vehicle has at least two energy sources. In most cases, the prime mover is an internal combustion engine (ICE) while the auxiliary energy source can be mechanical, electrical, pneumatic or hydraulic. It is expected from the control unit of a hybrid vehicle the use of the ICE in high efficiency working zones and to shut it down when it is more convenient, while using the EMG at partial loads and as a fast torque response during transients. However, the battery state of charge may represent a limitation for such a strategy. That’s the reason why, in most cases, energy management strategies are based on the State Of Charge, or SOC, control. Several studies have been conducted on this topic and many different approaches have been illustrated. The purpose of this dissertation is to develop an online (usable on-board) control strategy in which the operating modes are defined using an instantaneous optimization method that minimizes the equivalent fuel consumption of a hybrid electric vehicle. The equivalent fuel consumption is calculated by taking into account the total energy used by the hybrid powertrain during the propulsion phases. The first section presents the hybrid vehicles characteristics. The second chapter describes the global model, with a particular focus on the energy management strategies usable for the supervisory control of such a powertrain. The third chapter shows the performance of the implemented controller on a NEDC cycle compared with the one obtained with the original control strategy.