Multi-agent literature explores personifying artificial agents with personality, emotions or cognitive biases to produce “typical”, believable agents. In this study, we demonstrate the potential of endowing artificial agents with a motivation, using human implicit motivation psychology theory that introduces 3 motive profiles - power, achievement and affiliation, to create diverse, risk-aware agents. We first devise a framework to model these motivated agents (or agents with any inherent behavior), that can activate different strategies depending on the circumstances. We conduct experiments on a fire-fighting task domain, evaluate how motivated teams perform, and draw conclusions on appropriate team compositions to be deployed in environments with different risk levels. Our framework generates predictable agents as their resulting behaviors align with the inherent characteristics of their motives. We find that motivational diversity within teams is beneficial in dynamic collaborative environments, especially as the task risk level increases. Furthermore, we observed that the best composition in terms of the performance metrics used to evaluate team compositions, does not remain the same as the collaboration level required to achieve goals changes. These results have implications for future designs of risk-aware autonomous teams and Human-AI teams, as they highlight the prospects of creating better artificial teammates and performance gains that could be achieved through anthropomorphized motivated agents.

People often utilise online media (e.g., Facebook, Reddit) as a platform to express their psychological distress and seek support. State-of-the-art NLP techniques demonstrate strong potential to automatically detect mental health issues from text. Research suggests that mental health issues are reflected in emotions (e.g., sadness) indicated in a person’s choice of language. Therefore, we developed a novel emotion-annotated mental health corpus (EmoMent),consisting of 2802 Facebook posts (14845 sentences) extracted from two South Asian countries - Sri Lanka and India. Three clinical psychology postgraduates were involved in annotating these posts into eight categories, including ‘mental illness’ (e.g., depression) and emotions (e.g., ‘sadness’, ‘anger’). EmoMent corpus achieved ‘very good’ inter-annotator agreement of 98.3% (i.e. % with two or more agreement) and Fleiss’ Kappa of 0.82. Our RoBERTa based models achieved an F1 score of 0.76 and a macro-averaged F1 score of 0.77 for the first task (i.e. predicting a mental health condition from a post) and the second task (i.e. extent of association of relevant posts with the categories defined in our taxonomy), respectively.

Dengue is one of the deadly and fast spreading diseases in Sri Lanka. The female Aedes mosquito is the dengue vector and these mosquitoes breed in clear and non-flowing water. The Public Health Inspectors (PHIs) are tasked with detecting and eliminating such water collection areas.

The human-elephant conflict is causing significant damages to the life of human and elephants in Sri Lanka. Minimizing encounters between humans and elephants is hence crucial in alleviating the human-elephant conflict. We have earlier introduced Eloc, a cost-effective localization system that is based on infrasonic emissions from wild elephants. One of the remaining challenges is a resource-efficient method to detect the elephants' infrasonic emissions. We present our efforts on devising a support vector machine (SVM) that is able to detect elephant rumbles on Eloc nodes.

Machine learning techniques which are involved in knowledge extraction from stationary datasets have been becoming inefficient due to the dynamic nature of contemporary data spaces. Hence, machine learning research constantly investigates incremental learning techniques to address this requirement. However, it is always a challenge to uncover useful information incrementally from a non-stationary input space because of the complexity an algorithm introduces to counter the stability-plasticity dilemma. In order to facilitate this demand a learning model is proposed using the self-organization and competitive learning strategy. Moreover, an algorithm which is implemented based on the proposed model is also presented with the experimental results to prove the validity of the proposed learning model in a non-stationary context.