This dissertation summarises a research path aimed at fostering the use of Cognitive Architectures in Serious Games research field. Cognitive Architectures are an embodiment of scientific hypotheses and theories aimed at capturing the mechanisms of cognition that are considered consistent over time and independent of specific tasks or domains. The theoretical approaches provided by the research in computational cognitive modelling have been used to formalise a methodological framework to guide researchers and experts in the game-based education sector in designing, implementing, (...) and evaluating Serious Games. The investigation of cognitive processes involved during the game experience represents the fundamental step of the pro- posed approach. Two different case studies are described to discuss the possible use of the suggested framework. In the first case study, the aim was to design a modified version of the Tetris game with the intention of making the game more effective in training the visual-spatial skill called mental rotation. In the second scenario, the frame- work was used as a basis for creating an innovative persuasive game. This case study provides an example of adopting cognitive architectures for implementing a non-player character with human-like behaviour developed using targeted cognitive theories. (shrink)

We argue that cognitive models can provide a common ground between human users and deep reinforcement learning (Deep RL) algorithms for purposes of explainable artificial intelligence (AI). Casting both the human and learner as cognitive models provides common mechanisms to compare and understand their underlying decision-making processes. This common grounding allows us to identify divergences and explain the learner's behavior in human understandable terms. We present novel salience techniques that highlight the most relevant features in each model's decision-making, as well (...) as examples of this technique in common training environments such as Starcraft II and an OpenAI gridworld. (shrink)

This work is an initial step toward developing a cognitive theory of cyber deception. While widely studied, the psychology of deception has largely focused on physical cues of deception. Given that present‐day communication among humans is largely electronic, we focus on the cyber domain where physical cues are unavailable and for which there is less psychological research. To improve cyber defense, researchers have used signaling theory to extended algorithms developed for the optimal allocation of limited defense resources by using deceptive (...) signals to trick the human mind. However, the algorithms are designed to protect against adversaries that make perfectly rational decisions. In behavioral experiments using an abstract cybersecurity game (i.e., Insider Attack Game), we examined human decision‐making when paired against the defense algorithm. We developed an instance‐based learning (IBL) model of an attacker using the Adaptive Control of Thought‐Rational (ACT‐R) cognitive architecture to investigate how humans make decisions under deception in cyber‐attack scenarios. Our results show that the defense algorithm is more effective at reducing the probability of attack and protecting assets when using deceptive signaling, compared to no signaling, but is less effective than predicted against a perfectly rational adversary. Also, the IBL model replicates human attack decisions accurately. The IBL model shows how human decisions arise from experience, and how memory retrieval dynamics can give rise to cognitive biases, such as confirmation bias. The implications of these findings are discussed in the perspective of informing theories of deception and designing more effective signaling schemes that consider human bounded rationality. (shrink)