Grau, Ronald R (2022) Towards collaborative Human-AI systems for knowledge acquisition, modelling, and discovery across domain boundaries. In: 1st International Workshop on Knowledge Representation for Hybrid Intelligence, 14 Jun 2022, Amsterdam, Netherlands. (Accepted)

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Abstract

Collaborative Human-AI systems can help humans address hard problems that are currently difficult to investigate at the system level. Creating software that facilitates human knowledge specification through effective visual interfaces can be augmented with computational techniques to support the cognitive operations that are required for knowledge acquisition, modelling, and discovery of heterogeneous knowledge. For example, there are process systems that exhibit simultaneous physical, chemical, and biological changes at different physical and temporal scales. These systems exist both in nature (e.g., climate science) and in industry (e.g., food production), with interactions that are complex and dynamic in many ways, spanning multiple areas of science. The related knowledge tends to be incomplete, exists at different levels of granularity and abstraction, and is expressed using various concepts, notations and paradigms that may be difficult to represent and reason over in combination. This paper is about a conceptual framework that comprises a representational system, meta model, as well as computational and cognitive methods aimed at assisting humans in the construction of knowledge models, and applied in a food processing domain. Following a representational epistemic approach, interactive diagrams were designed and implemented within a software suite that support cognitive operations for compositional knowledge specification. The software uses various techniques to augment human cognitive abilities, for instance by propagating and making visible changes in the system, across different levels of abstraction, covering multiple perspectives (e.g., compositional structure, changing properties, functional impact). Algorithms and visual cues are also used for guiding human knowledge operations, e.g., by generating new interaction contexts automatically, based on modelling decisions. Key to the success of the system has been how it interfaces with humans at the knowledge level to provide effective means for them to inspect and interact with the model.

Item Type:	Conference or Workshop Item (Paper)
Keywords:	Intelligence augmentation, Collaborative modelling, Diagrammatic representation and reasoning
Schools and Departments:	School of Engineering and Informatics > Informatics
Research Centres and Groups:	Creative Technology
Subjects:	Q Science > QA Mathematics > QA0075 Electronic computers. Computer science > QA0076 Computer software Q Science > QA Mathematics > QA0075 Electronic computers. Computer science > QA0076.9.A-Z Other topics, A-Z > QA0076.9.H85 Human-computer interaction
Related URLs:	https://sites.google.com/view/kr4hi/prog...
Depositing User:	Ron Grau
Date Deposited:	04 Aug 2022 16:52
Last Modified:	04 Aug 2022 16:52
URI:	http://sro.sussex.ac.uk/id/eprint/107262

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