Mental model ascription – also called mindreading – is the process of inferring the mental states of others, which happens as a matter of course in social interactions. But although ubiquitous, mindreading is presumably a highly variable process: people mindread to different extents and with different results. We hypothesize that human mindreading ability relies on a large number of personal and contextual features: the inherent abilities of specific individuals, their current physical and mental states, their knowledge of the domain of discourse, their familiarity with the interlocutor, the risks associated with an incorrect assessment of intent, and so on. This paper presents a theory of mindreading that models diverse artificial intelligent agents using an inventory of parameters and value sets that represent traits of humans and features of discourse contexts. Examples are drawn from Maryland Virtual Patient, a prototype system that will permit medical trainees to diagnose and treat cognitively modeled virtual patients with the optional assistance of a virtual tutor. Since real patients vary greatly with respect to physiological and cognitive features, so must a society of virtual patients. Modeling such variation is one of the goals of the overall OntoAgent program of research and development.
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Cited by
Cited by 6 other publications
McShane, Marjorie
2017. Natural Language Understanding (NLU, not NLP) in Cognitive Systems. AI Magazine 38:4 ► pp. 43 ff.
McShane, Marjorie, Sergei Nirenburg, Valerio Basile, Tommaso Caselli & Daniele P. Radicioni
2019. Context for language understanding by intelligent agents. Applied Ontology 14:4 ► pp. 415 ff.
Nirenburg, Sergei & Marjorie McShane
2015. The Interplay of Language Processing, Reasoning and Decision-Making in Cognitive Computing. In Natural Language Processing and Information Systems [Lecture Notes in Computer Science, 9103], ► pp. 167 ff.
Nirenburg, Sergei, Marjorie McShane, Stephen Beale, Peter Wood, Brian Scassellati, Olivier Magnin & Alessandro Roncone
2018. Toward Human-Like Robot Learning. In Natural Language Processing and Information Systems [Lecture Notes in Computer Science, 10859], ► pp. 73 ff.
2016. Now you feel it, now you don’t. Interaction Studies. Social Behaviour and Communication in Biological and Artificial Systems 17:2 ► pp. 211 ff.
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