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Sökning: swepub > Konferensbidrag > Göteborgs universitet > Lunds universitet > Engelska > Balkenius Christian

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1.
  • Strannegård, Claes, 1962, et al. (författare)
  • Transparent Neural Networks: Integrating Concept Formation and Reasoning
  • 2012
  • Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349 .- 0302-9743. - 9783642355059 - 9783642355066 ; 7716, s. 302-311
  • Konferensbidrag (refereegranskat)abstract
    • We present the transparent neural networks, a graph-basedcomputational model that was designed with the aim of facilitating humanunderstanding. We also give an algorithm for developing such networksautomatically by interacting with the environment. This is doneby adding and removing structures for spatial and temporal memory.Thus we automatically obtain a monolithic computational model whichintegrates concept formation with deductive, inductive, and abductivereasoning.
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2.
  • Lowe, Robert, et al. (författare)
  • Vicarious Value Learning and Inference in Human-Human and Human-Robot Interaction
  • 2019
  • Ingår i: 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW 2019. - 9781728138923 - 9781728138916
  • Konferensbidrag (refereegranskat)abstract
    • © 2019 IEEE. Among the biggest challenges for researchers of human-robot interaction is imbuing robots with lifelong learning capacities that allow efficient interactions between humans and robots. In order to address this challenge we are developing computational mechanisms for a humanoid robotic agent utilizing both system 1 and system 2-like cognitive processing capabilities. At the core of this processing is a Social Affective Appraisal model that allows for vicarious value learning and inference. Using a multi-dimensional reinforcement learning approach the robotic agent learns affective value-based functions (system 1). This learning can ground representations of affective relations (predicates) relevant to interacting agents. In this article we discuss the existing theoretical basis for developing our neural network model as a system 1-like process. We also discuss initial ideas for developing system 2-like top-down/generative affective (semantic relation-based) processing. The aim of the symbolic-connectionist architectural development is to promote autonomous capabilities in humanoid robots for interacting efficiently/intelligently (recombinant application of learned associations) with humans in changing and challenging environments.
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refereegranskat (2)
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Balkenius, Chris... (2)Ta bort avgränsningen
Wessberg, Johan, 196 ... (1)
Almér, Alexander, 19 ... (1)
Häggström, Olle, 196 ... (1)
Strannegård, Claes, ... (1)
Lowe, Robert (1)
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Gander, Pierre, 1971 (1)
Bach, Joscha (1)
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