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Augmenting Human Intelligence in Goal Oriented Tasks

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Next Generation Data Technologies for Collective Computational Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 352))

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Abstract

Tell me and I forget, teach me and I remember, involve me and I learn. (Benjamin Franklin). The world is an increasingly complex with problems that require swift resolution. Although knowledge is widely available, be it stored in companies databases or spread over the Internet, humans have intrinsic limitations for handling very large volumes of information or keeping track of frequent updates in a constantly changing world. Moreover, human reasoning is highly intuitive and potentially biased due to time pressure and excess of condence. Computer systems that manage knowledge by thoroughly exploring the context and range of alternatives may improve human decision-making by making people aware of possible misconceptions and biases. Computer systems are also limited in their potential usage due to the frame problem. Systems are not aware of their ignorance, thus they cannot substitute human intelligence; however, they may provide a useful complement. The objective of this chapter is to present the AGUIA model for amplifying human intelligence, utilizing agents technology for task-oriented contexts. AGUIA uses domain ontology and task scripts for handling formal and semiformal knowledge bases, thereby helping to systematically (1) explore the range of alternatives; (2) interpret the problem and the context; and (3) maintain awareness of the problem. As for humans, knowledge is a fundamental resource for AGUIA performance. AGUIAs knowledge base remains in the background and keeps updating its content during interaction with humans, either through identied individuals or through anonymous mass contribution. The feasibility and benets of AGUIA were demonstrated in many different elds, such as engineering design, fault diagnosis, accident investigation and online interaction with the government. The experiments considered a set of criteria including: product cost, number of explored alternatives, users problem understanding and users awareness of problem context changes. Results indicate that AGUIA can actually improve human problem solving capacity in many different areas.

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Author information

Authors and Affiliations

  1. Instituto de Computação, Universidade Federal Fluminense, Niteroi, Rio de Janeiro, Brasil

    Ana Cristina Bicharra Garcia

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  1. Ana Cristina Bicharra Garcia
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Editors and Affiliations

  1. School of Computing & Maths, University of Derby, DE22 1GB, Derby, United Kingdom (UK)

    Nik Bessis

  2. Dept de Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, Barcelona, Spain

    Fatos Xhafa

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Garcia, A.C.B. (2011). Augmenting Human Intelligence in Goal Oriented Tasks. In: Bessis, N., Xhafa, F. (eds) Next Generation Data Technologies for Collective Computational Intelligence. Studies in Computational Intelligence, vol 352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20344-2_11

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  • DOI: https://doi.org/10.1007/978-3-642-20344-2_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20343-5

  • Online ISBN: 978-3-642-20344-2

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