The article below builds on some old ideas discussed in my old ECAL 2001 paper, Evolution, Adaptation, and Behavioural Holism in Artificial Intelligence and also included in my PhD dissertation Behaviour-Based Learning: Evolution-Inspired Development of Adaptive Robot Behaviours. They were originally a comment on behavior-based robotics as presented in Rodney Brooks's papers A Robust Layered Control System for a Mobile Robot and Elephants Don't Play Chess, and in Ronald C. Arkin's book Behavior-based Robotics.
These ideas gained new relevance recently when I participated in a the Challenges for Artificial Cognitive System II workshop arranged by the European Network for the Advancement of Artificial Cognitive Systems, Interaction and Robotics (EUCogII). Some ideas from that workshop were captures in the workshop wiki. Below I summarize what I took from the workshop.
There is a history, in the science of AI and its related fields, to look a problems in isolation and to simplify problems to the extent where the solutions do contribute significantly to our knowledge of cognition. Three examples are symbolic problem solving, Computer Vision and Speech Recognition. While each of these areas have produced valuable technologies, in their own right, they have also, by narrowing their focus, removed themselves so far from the problems faced by animals, including humans, that their solutions have not, to any great extent, helped our understanding of cognitive systems. One cannot criticize this work for simplifying and narrowing their studies, as this is a necessary part of developing working solutions to given problems. The specificity of their solutions however, raises the question of whether it is possible to develop systems that both solve a specific problem, and also provide key insights into cognition. Cognition is, arguably, the ability to apply a general understanding of the world to new problems and situations, and, if so, cognition is generalization rather than specialization.
A generalist approach to modelling cognition raises two fundamental questions:
- Generalize across what?
- What kind of problems can provide tractable challenges for generalist cognitive systems?
By challenges being tractable we mean that it is possible to imagine solutions based on the incremental development or integration of existing technologies. There are many examples of interesting but intractable challenges for generalist cognitive systems. Most challenges requiring human-like cognitive abilities such as autonomous robot workers or companions are clearly still intractable, but many challenges which have, arguably, lower cognitive requirements, e.g., robotic sheep dogs, guard dogs, steeds or even pack animals, also look intractable w.r.t. many of the sub-problems they contain.
Beyond finding tractable challenges it is also interesting to consider whether we can identify a sequence of challenges that could form milestones along a path of increasingly high levels of generalist cognitive abilities. Following from the second question, we can also ask whether any such problems would be practical, by which we mean that solving them would provide a technology that could be useful to society.
Recognizing that cognition is dependent on physiology introduces a number of physiological dimensions of cognition:
- Sensors; Vision (stereo, colour), audition (stereo), proprioceptive, tactile
- Actuators; Muscles, Legs, arms, hands, thumbs
- Nervous system; Spine, limbic system, cortical architecture
Neil R. Carlson's Physiology of Behavior is a good introduction to sensors, actuators and related physiology. The architecture of the complete brain is described in Larry W. Swanson's book Brain Architecture: Understanding the Basic Plan. The cortical architecture is discussed in Joaquin M Fuster's book Cortex and Mind: Unifying Cognition.
The environment is also a crucial actor in enabling or prohibiting intelligent behaviour. I have divided this into:
- Physical environment
- Social environment
A book that discusses some issues in this respect is John Alcock's Animal Behavior: An Evolutionary Approach.
Anette Karmiloff-Smith, in her book Beyond Modularity: A developmental perspective on cognitive science builds on traditional developmental approaches such as those of Piaget and Fodor to suggest that a child's cognitive development takes place within five different domains before the process of representational redescription produces domain generic knowledge from the previous domain specific knowledge. Anette Karmiloff-Smith considers the following domains:
- The child as a linguist
- The child as a physicist
- The child as a mathematician
- The child as a psychologist
- The child as a notator
Looking for dimensions of cognition, Stephen Mithen, in his book The prehistory of the mind, 1996, suggests a number of specialized intelligences that act as a foundation, or 'chapels' around an initial general intelligence. On top of these chapels, the 'superchapel' of meta-representation is then built, to provide the cognitive capabilities of modern humans. The specialized intelligences suggested by Steven Mithen are:
- Technical intelligence
- Natural history intelligence
- Social intelligence
- Linguistic intelligence
Finally, in the book A Roadmap for Cognitive Development in Humanoid Robots, David Vernon, Claes von Hofsten and Luciano Fadiga have used knowledge from human cognitive development to define a road map for cognitive development in humanoid robots. This work is very much in the spirit of what I suggest here, but I would like to consider a wider scientific that will give us a better chance of identifying realistic milestones.
The Kizmet robot was developed at MIT and used in a wide range of research activities.
The iCub robot is a popular humanoid research robot that has also been used for a wide range of research activities. As a result, it also has a well developed cognitive architecture.