| 1. |
- Strannegård, Claes, 1962, et al.
(författare)
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A Cognitive Architecture Based on Dual Process Theory
- 2013
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Ingår i: Lecture Notes in Computer Science. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 0302-9743 .- 1611-3349. ; 7999, s. 140-149
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a cognitive architecture based on Kahneman’s dual process theory [1]. The long-term memory is modeled as a transparent neural network that develops autonomously by interacting with the environment. The working memory is modeled as a buffer containing nodes of the long-term memory. Computations are defined as processes in which working memory content is transformed according to rules that are stored in the long-term memory. In this architecture, symbolic and subsymbolic reasoning steps can be combined and resource-bounded computations can be defined ranging from formal proofs to association chains. Key words: cognitive architecture, dual process theory, computation, transparent neural network.
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| 2. |
- Strannegård, Claes, 1962, et al.
(författare)
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Emotional Concept Development
- 2015
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Ingår i: Lecture Notes in Computer Science. Artificial General Intelligence: 8th International Conference, AGI 2015, AGI 2015, Berlin, Germany, July 22-25, 2015, Proceedings /eds. Bieger, Jordi, Ben Goertzel, and Alexey Potapov. - Cham : Springer International Publishing. - 0302-9743 .- 1611-3349. - 9783319213651
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Konferensbidrag (refereegranskat)abstract
- Artificial emotions of different varieties have been used for controlling behavior, e.g. in cognitive architectures and reinforcement learning models. We propose to use artificial emotions for a different purpose: controlling concept development. Dynamic networks with mechanisms for adding and removing nodes are more flexible than networks with a fixed topology, but if memories are added whenever a new situation arises, then these networks will soon grow out of proportion. Therefore there is a need for striking a balance that ideally ensures that only the most useful memories will be formed and preserved in the long run. Humans have a tendency to form and preserve memories of situations that are repeated frequently or experienced as emotionally intense (strongly positive or strongly negative), while removing memories that do not meet these criteria. In this paper we present a simple network model with artificial emotions that imitates these mechanisms.
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| 3. |
- Strannegård, Claes, 1962, et al.
(författare)
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Transparent Neural Networks: Integrating Concept Formation and Reasoning
- 2012
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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
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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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| 4. |
- Andersson, Patrick, et al.
(författare)
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Exploration strategies for homeostatic agents
- 2019
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 11654 LNAI, s. 178-187
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Konferensbidrag (refereegranskat)abstract
- This paper evaluates two new strategies for investigating artificial animals called animats. Animats are homeostatic agents with the objective of keeping their internal variables as close to optimal as possible. Steps towards the optimal are rewarded and steps away punished. Using reinforcement learning for exploration and decision making, the animats can consider predetermined optimal/acceptable levels in light of current levels, giving them greater flexibility for exploration and better survival chances. This paper considers the resulting strategies as evaluated in a range of environments, showing them to outperform common reinforcement learning, where internal variables are not taken into consideration.
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| 5. |
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| 6. |
- Engström, Fredrik, 1977, et al.
(författare)
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Generating Comprehensible Explanations in Description Logic
- 2014
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Ingår i: 27th International Workshop on Description Logics. Vienna, Austria, July 17-20, 2014. - 1613-0073.
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Konferensbidrag (refereegranskat)abstract
- We propose a method for generating comprehensible explanations in description logic. Such explanations could be of potential use for e.g. engineers, doctors, and users of the semantic web. Users commonly need to understand why a logical statement follows from a set of hypotheses. Then, automatically generated explanations that are easily understandable could be of help. A proof system for description logic that can be used for generating comprehensible explanations is proposed. Similar systems have been proposed for propositional logic [30] and first-order logic [28].
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| 7. |
- Hernandez-Orallo, J., et al.
(författare)
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A New AI Evaluation Cosmos: Ready to Play the Game?
- 2017
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Ingår i: AI Magazine. - : Wiley. - 0738-4602 .- 2371-9621. ; 38:3, s. 66-69
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Tidskriftsartikel (refereegranskat)abstract
- We report on a series of new platforms and events dealing with AI evaluation that may change the way in which AI systems are compared and their progress is measured. The introduction of a more diverse and challenging set of tasks in these platforms can feed AI research in the years to come, shaping the notion of success and the directions of the field. However, the playground of tasks and challenges presented there may misdirect the field without some meaningful structure and systematic guidelines for its organization and use. Anticipating this issue, we also report on several initiatives and workshops that are putting the focus on analyzing the similarity and dependencies between tasks, their difficulty, what capabilities they really measure and ultimately on elaborating new concepts and tools that can arrange tasks and benchmarks into a meaningful taxonomy.
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| 8. |
- Johannesson, Louise, et al.
(författare)
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Basic language learning in artificial animals
- 2019
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Ingår i: Advances in Intelligent Systems and Computing. - Cham : Springer International Publishing. - 2194-5365 .- 2194-5357. - 9783319993157 ; 848, s. 155-161
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Konferensbidrag (refereegranskat)abstract
- We explore a general architecture for artificial animals, or animats, that develops over time. The architecture combines reinforcement learning, dynamic concept formation, and homeostatic decision-making aimed at need satisfaction. We show that this architecture, which contains no ad hoc features for language processing, is capable of basic language learning of three kinds: (i) learning to reproduce phonemes that are perceived in the environment via motor babbling; (ii) learning to reproduce sequences of phonemes corresponding to spoken words perceived in the environment; and (iii) learning to ground the semantics of spoken words in sensory experience by associating spoken words (e.g. the word “cold”) to sensory experience (e.g. the activity of a sensor for cold temperature) and vice versa.
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| 9. |
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| 10. |
- Johansson, Anton, 1993, et al.
(författare)
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Exact spectral norm regularization for neural networks
- 2022
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- We pursue a line of research that seeks to regularize the spectral norm of the Jacobian of the input-output mapping for deep neural networks. While previous work rely on upper bounding techniques, we provide a scheme that targets the exact spectral norm. We showcase that our algorithm achieves an improved generalization performance compared to previous spectral regularization techniques while simultaneously maintaining a strong safeguard against natural and adversarial noise. Moreover, we further explore some previous reasoning concerning the strong adversarial protection that Jacobian regularization provides and show that it can be misleading.
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