Re-representation in Cognitive Systems

Re-representation is a cognitive ability of great relevance to reasoning, problem solving, creativity and knowledge discovery, and to the fields of cognitive psychology, artificial intelligence, computational creativity, cognitive robotics and design. Appearing under different names in the literature - re-representation, representational change and restructuring - this concept refers to natural cognitive systems’ability to represent existing perceived features or knowledge in new ways. Ill-structured problems can benefit from an agent’s ability to re-represent problems in multiple ways, until an insightful representation that leads to a solution is found. Most real-world problems are ill-structured until we formalize and structure them, and both AI and robotics would benefit from robust abilities for re-representation. The design community has previously addressed ill structured problem solving and built several models of re-representation.

This Research Topic will aim to reunite both theoretical foundations and applied research which focuses on the abilities for re-representation of natural and artificial cognitive systems. Re-representation is a topic of major interest to four communities:
- Cognitive psychology, which can use theoretical advances and computational implementations of re-representation, in order to test competing models and hypotheses;
- Artificial intelligence, which can use re-representation for the next generation of multi pattern recognition, data mining and knowledge discovery applications, cognitive systems and cognitive agents;
- Cognitive robotics, which is recently focusing on creative tool use incorporating perspectives from developmental psychology and
- Design, which has applied classical theories of problem solving to propose the design process as a solution to ill structured problem solving.

This Research Topic will gather the perspectives and newest work on re-representation from all four communities. This will foster interdisciplinary dialogue, and enable us to design computational models and computational systems able to tackle re-representation a wider variety of contexts.

Subtopics of interest will be related (but not limited) to:
• Re-representation
• Human creative cognition
• Problem solving
• Knowledge discovery
• Memory and re-representation
• Computational creativity and re-representation
• Re-representation in design
• Re-representation in robotics
• Creative artificial intelligence and representational redescription
• Cognitive modelling of representational change
• (Creative) Reasoning and representational change
• Data mining, knowledge discovery and re-representation
• Theories of re-representation and restructuring
• Knowledge representation and re-representation
• Image schemas for re-representation
• Computational processes for re-representation
• Pattern recognition and re-representation
• Re-representation and developmental systems