LAUSR

As technological advancements increase exponentially, mobile phones become smarter with machine learning and artificial intelligence algorithms. These advancements have allowed mobile phone users to perform most of their daily routine tasks on mobile phones; tasks performed in daily routines are called repetitive tasks and are performed manually by the users themselves. However, machine learning and artificial intelligence have enabled those tasks to be performed automatically, known as task automation. The users can perform task automation, e.g., through creating automation rules or an intelligent agent, e.g., conversational agents, virtual personal assistants, etc. Several techniques to achieve task automation have been proposed, but this review shows that task automation by programming by demonstration has had massive developmental growth because of its user-centered approach. Apple Siri, Google Assistant, MS Cortana, and Amazon Alexa are the most known task automation agents. However, these agents are not widely adopted because of their usability issues. In this study, two research questions are evaluated through the available literature to expand the research on intelligent task automation agents: (1) What is the state-of-the-art in task automation agents? (2) What are the existing methods and techniques for developing usability heuristics, specifically for intelligent agents? Research shows groundbreaking developments have been made in mobile phone task automation recently. However, it must still be conducted per usability principles to achieve maximum usability and user satisfaction. The second research question further justifies developing a set of domain-specific usability heuristics for mobile task automation intelligent agents.