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Dialogues between humans are complex due to the challenges in predicting how they will unfold as people may want to achieve different purposes. For instance, to act together, they co-create a common goal; to learn, they co-create knowledge; to build relationships, they share emotions and beliefs. Apart from different purposes, people may want to achieve multiple purposes in a dialogue, introducing a movement between goals. Such actions cause problems in understanding and conflicts among the participants. Activity Theory denotes such situations as breakdown situations, which also occur when people have dialogues with software agents driven by Artificial Intelligence (AI). This thesis falls within the domain of human-centred AI, focusing on software agents able to collaborate and support people to achieve their goals. We call these software agents socially intelligent agents.

This thesis has two aims: (1) to develop an increased understanding of breakdown situations in dialogues between humans and socially intelligent agents and (2) to develop computational frameworks based on the developed understanding to manage breakdown situations, which could be embedded in an agent's cognitive architecture. The theoretical frameworks from social sciences, particularly Activity Theory, were applied to address the aims. They provided an alternate perspective that considers breakdown situations as opportunities to learn something new rather than the traditional view of them being errors or failures.

The main contributions addressing the first aim were theory-driven analysis and empirical findings that provided increased knowledge of breakdown situations, resulting in design implications and future agendas guiding the subsequent research. The results informed the three strategies to manage breakdown situations by aligning, partially aligning or not aligning with human's intentions. We found that participants considered partial alignment as a sufficient level of agreement for potential collaboration, which would be interesting to verify in future studies. To address the second aim, two novel computational frameworks were provided. These frameworks were based on linguistics and social sciences theories, allowing an agent to interpret the dialogue's syntax, semantics, and social aspects, facilitating a deeper understanding of dialogues. Finally, a novel computational framework was developed to reason about conflicts and be able to plan by adopting the strategy of aligning with the human's intentions. 

We conceptualised a cognitive architecture based on our research findings. The cognitive architecture embeds mechanisms for socially intelligent agents to manage breakdown situations in dialogues with humans.