Adaptive Behavior

The study and simulation of adaptive behavior in natural and artificial systems has always involved the convergence of several disciplines, interests, and methods. Since its inception in 1992, the pages of this journal have reflected a cross-fertilization between the sciences of the artificial, the sciences of living systems, and the sciences of the mind. As a result, Adaptive Behavior has been, and continues to be, a forum for innovative, creative, yet rigorous and peer-reviewed work on complex adaptive systems, robotic and computational investigations of behavior and cognition, as well as novel theoretical developments and applications.

The general mission of Adaptive Behavior has not changed fundamentally even as the journal, like any good adaptive system, assimilates and accommodates to new challenges and open questions. Accordingly, our particular aims are constantly on the move, as they are driven no only by general advances in knowledge, as occurs within any well-defined research discipline, but also by the birth of new research programs out of the stimulating intellectual milieu of interdisciplinary debate and collaboration. A key purpose of this journal is to facilitate such creative work by being the source of new ideas, the forum for novel recombination, and a place to ask difficult questions that are rarely asked at the core of individual disciplines.

Realizing these goals means encouraging high-quality publications and debate in several exciting and emerging research areas. In particular, the journal aims to contribute to the consolidation of new approaches to cognitive science, especially research related to the consolidation of new approaches to cognitive science, especially research related to "4E cognition" (embodied, embedded, extended, and enactive cognition), including the predictive coding framework, autopoietic and sensorimotor theory, as well as dynamical and ecological approaches to psychology. This journal is equally a fitting home for expanding research on the possibilities of intelligence without a central nervous system, such as behavior-based approaches to the origin of life, plant cognition and the adaptive capacities of multi-agent and social systems. Another important area is living technology, which includes morphological computation, deep neural networks, soft robotics, and other advances in the methods and practical applications of bio-inspired robotics and self-optimization. 

In particular, we identify the following research challenges:

- To better understand the adaptive and cognitive capacities of (bio-)chemical systems
- To concretize predictive coding into a framework that can be more easily applied to advancing actual examples of cognitive robotics
- To replicate biological autonomy in artificial systems (or to demonstrate why this cannot be done)
- To determine whether the various new approaches to the science of mind are compatible or, alternatively, to determine their competing predictions
- To better understand what (if any) are the limits of intelligence without a nervous system and intelligence without representations
- To clarify the nature of the normativity inherent in living systems in such a way that it could improve cognitive robotics and living technology
- To better understand the conditions under which multi-agent and social systems generate collective properties that benefit their components
- To search for new materials that allow for more adaptive robot bodies

Contributions that address one or more of these research challenges are particularly welcomed. 

Submissions from the general area of machine learning will be returned without review unless the findings have clear scientific relevance.