New Generation Computing - Call for Papers on “World Models for Intelligence”
Paper Submission Deadline: 31 October 2022
Publication Date: Accepted papers will be made available online on the Springer website shortly after acceptance. The publication date of the printed version will be determined when the Special Issue is finalized.
Aim and Scope
Since the rapid development of deep learning starting in the late 2010s, research on learning data-based models of the external world and their use for cognition and behavioral tasks has greatly spread. Given this, this special issue deals with topics related to artificial intelligence research (especially machine learning) in which systems demonstrate their intelligence capability by using acquired models of the world. Specifically, including description methods and description languages for modeling various aspects of the world, learning methods and their algorithms for abstracting and extracting various aspects of the world, as well as computational techniques for making various inferences and predictions using the obtained models. There are also contributions on testbeds and evaluation suites, such as simulators for training and testing, and methods for evaluating general intelligence. We welcome the emphasis on research that tries to tackle various aspects that have not yet been realized despite the advances in deep learning, and the use of knowledge from cognitive science and neuroscience for this purpose. On the other hand, we also welcome contributions that organize and clarify the nature of the advances in intelligent technology through the advancement of deep learning. We hope that the many contributions to this special issue will help open the door to intelligence as versatile as humans, beyond the limitations of traditional symbolic AI and deep learning that have been developed so far.
Topics of interest include, but are not limited to:
- Algorithmic information theory
- Brain-inspired intelligence
- Causal reasoning
- Cognitive robotics
- Common sense learning
- Consciousness mechanism
- Deep reinforcement learning
- Disentanglement
- Distillation
- Dual process theory
- Equivariance
- Equivalent structure
- Free Energy Principle
- General intelligence testing and assessment
- Generative models
- Learning environment platforms
- Imagination
- Imitation Learning
- Inductive bias
- Intelligent Architecture
- Intention estimation
- Inverse reinforcement learning
- Language usage
- Metacognition
- Multi-modality
- Object file theory
- Ontology
- Physics Simulator
- Planning
- Predictive Coding
- Probabilistic model
- Representation Learning
- Real time system
- Self-awareness
- Self-supervised learning
- Situation decomposition
- Versatility of animal intelligence
- Working memory
- World Model
Important Dates
Submission deadline: 31 October 2022
Authors’ notification: 31 January 2023
Revisions due: 31 March 2023
Final decision: 31 May 2023
Submission Guidelines
Authors should prepare their manuscript according to the submission guidelines (this opens in a new tab). All papers must be submitted to the journal's submission system (this opens in a new tab).
During the submission stage, please select “Yes” for the question “Does this manuscript belong to a special feature?” in the Additional Information tab, then select the special feature “S.I. : World Models for Intelligence”.
Guest Editors
Yutaka Matsuo, Professor
Department of Technology Management for Innovation School of Engineering
The University of Tokyo
E-mail (this opens in a new tab) / Web (this opens in a new tab)
Mingbo Cai, Assistant Professor
International Research Center for Neurointelligence
The University of Tokyo
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Hiroshi Yamakawa
Department of Technology Management for Innovation School of Engineering
The University of Tokyo
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Yusuke Iwasawa
Department of Technology Management for Innovation School of Engineering
The University of Tokyo
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Masahiro Suzuki
Department of Technology Management for Innovation School of Engineering
The University of Tokyo
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Wataru Kumagai
Department of Technology Management for Innovation School of Engineering
The University of Tokyo
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