Intention recognition entails the process of becoming aware of another agent’s intention by inferring it through its actions and their effects on the environment. It allows agents to prevail when interacting with others in both cooperative and hostile environments. One of the main challenges in intention recognition is generating and collecting large amounts of data, and then being able to infer and recognise strategies. To this aim, in the context of repeated interactions, we generate diverse datasets, characterised by various noise levels and complexities. We propose an approach using different popular machine learning methods to classify strategies represented by sequences of actions in the presence of noise. Experiments have been conducted by varying the noise level and the number of generated strategies in the input data. Results show that the adopted methods are able to recognise strategies with high accuracy. Our findings and approach open up a novel research direction, consisting of combining machine learning and game theory in generating large and complex datasets and making inferences. This can allow us to explore and quantify human behaviours based on data-driven and generative models.

Before embarking on a new collective venture, it is important to understand partners’ preferences and intentions and how strongly they commit to a common goal. Arranging prior commitments of future actions has been shown to be an evolutionary viable strategy in the context of social dilemmas. Previous works have focused on simple well-mixed population settings, for ease of analysis. Here, starting from a baseline model of a coordination game with asymmetric benefits for technology adoption in the well-mixed setting, we examine the impact of different population structures, including square lattice and scale-free (SF) networks, capturing typical homogeneous and heterogeneous network structures, on the dynamics of decision-making in the context of coordinating technology adoption. We show that, similarly to previous well-mixed analyses, prior commitments enhance coordination and the overall population payoff in structured populations, especially when the cost of commitment is justified against the benefit of coordination, and when the technology market is highly competitive. When commitments are absent, slightly higher levels of coordination and population welfare are obtained in SF than lattice. In the presence of commitments and when the market is very competitive, the overall population welfare is similar in both lattice and heterogeneous networks; though it is slightly lower in SF when the market competition is low, while social welfare suffers in a monopolistic setting. Overall, we observe that commitments can improve coordination and population welfare in structured populations, but in its presence, the outcome of evolutionary dynamics is, interestingly, not sensitive to changes in the network structure.