Embodied cognitive science

Embodied Cognitive Science is an interdisciplinary field of research whose aim is to explain the mechanisms underlying intelligent behavior. It comprises three main methodologies. (1). The modeling of psychological and biological systems in a holistic manner that considers the mind and body as a single entity. (2). The formation of a common set of general principles of intelligent behavior. (3). The experimental use of robotic agents in controlled environments.

Embodied Cog Sci borrows heavily from the philosophy of Embodiment and research fields related to this philosophy, namely Psychology, Neuroscience and Artificial Intelligence. Researchers in Embodied Cog Sci occasionally do talk about issues of free will and anthropomorphism.

Several of the roots of ECS can be traced to the philosopher Mark Johnson and the linguist George Lakoff, whose approaches to language and cognition reflect Turing's insight that it might alternately be proposed that a machine needs a human-like body to think and speak: It can also be maintained that it is best to provide the machine with the best sense organs that money can buy, and then teach it to understand and speak English. That process could follow the normal teaching of a child. Things would be pointed out and named, etc. Again, I do not know what the right answer is, but I think both approaches should be tried (Turing, 1950).

The embodied approach to the study of cognition is exemplified by the research and work of the late Francisco Varela of CNRS. The character of Varela's work demonstrates the interdisciplinary nature of the embodied approach. From the perspective of neuroscience, research in this field was led by Gerald Edelman of the Neurosciences Institute at La Jolla, and J. A. Scott Kelso of FAU. From the perspective of psychology, research by Michael Turvey and Eleanor Rosch. From the perspective of language acquisition, Eric Lenneberg and Philip Rubin at Haskins Laboratories. From the perspective of autonomous agent design, early work is sometimes attributed to Rodney Brooks or Valentino Braitenberg. From the perspective of artificial intelligence, see Understanding Intelligence by Rolf Pfeifer and Christian Scheier.

General principles of intelligent behavior
In the formation of general principles of intelligent behavior, Pfeifer intended to be contrary to older principles given in Traditional Artificial Intelligence. The most dramatic difference is that the principles are applicable only to situated robotic agents in the real world, a domain where Traditional Artificial Intelligence showed the least promise.

Principle of Parallel, Loosely-coupled Processes ::  An alternative to hierarchical methods of knowledge and action selection. This design principle differs most importantly from the Sense-Think-Act cycle of traditional AI. Since it does not involve this famous cycle, it is not affected by the Frame problem.

Principle of Sensory-Motor Coordination :: Ideally, internal mechanisms in an agent should give rise to things like memory and choice-making in an emergent fashion, rather than being prescriptively programmed from the beginning. These kinds of things are allowed to emerge as the agent interacts with the environment. The motto is, build less assumptions into the agent's controller now, so that learning can be more robust and idiosyncratic in the future.

Principle of Cheap Design and Redundancy :: Cheap design is a wink to the mass production of robots, and also a reference to the fact that an agent can still exhibit surprising behavior without a lot of internal processing.

Principle of Ecological Balance :: This is more a theory than a principle, but its implications are widespread. Its claim is that the internal processing of an agent cannot be made more complex unless there is a corresponding increase in complexity of the motors, limbs, and sensors of the agent. In other words, the extra complexity added to the brain of a simple robot will not create any discernable change in its behavior. The robot's morphology must already contain the complexity in itself to allow enough "breathing room" for more internal processing to develop.

The Value Principle :: This was the architecture developed in the Darwin III robot of Gerald Edelman. It relies heavily on connectionism.