Abstract: Researchers that study Human-Like computing mainly aim to understand how systems can emulate human cognitive performance. However, when Human-Like systems are designed for sharing economy applications in which humans have to collaborate in order to achieve a desired task, there are several problems that needs to be addressed before asking how cognitive performance of a single human can be emulated. In this paper, we highlight these problems, provide examples related to the ridesharing scenario, and introduce how we approach these problems and which techniques we are using to tackle them.

Citation: S.Ceppi. The Dilemma of Human-Like Collective Systems. In S. Muggleton et al, editor, Twentieth Workshop on Machine Intelligence (MI 20), Windsor Park, UK, 23-25 October, 2016. In press.

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Abstract: While AI has recently produced impressive systems that achieve human-like performance at challenging tasks, these systems tell us very little about how human intelligence works. In particular, they do not address the problem of composing knowledge and behaviour incrementally – a phenomenon that is pervasive in individual and collective human intelligence. We argue that achieving more human-like AI requires focusing on diversity in reasoning and behaviour among humans and artificial agents, and that developing systems capable of dealing with such diversity is key to achieving more human-like AI. In these systems intelligence should not only be measured in terms of how a system performs at a certain task, but also in terms of the properties of the process by which each component combines its knowledge and behaviour with that of others, just like humans do.

Citation: M. Rovatsos. Diversity-Awareness – The Key to Human-Like Computing? In S. Muggleton et al, editor, Twentieth Workshop on Machine Intelligence (MI 20), Windsor Park, UK, 23-25 October, 2016. In press.

Download: http://bit.ly/2jd0oMI