Cognitive Robotics

Artificial Intelligence (AI) is a domain of computer science focused on devices that are able to take some level of agency in achieving novel goals. Beyond that detail, the scope of AI is disputed, in part because “novel goals” is a moving target … as once-unimaginable feats become routine. Commonly mentioned aspects include abilities to collect relevant information about the environment, draw inferences based on that information, and apply those inferences flexibly.

Embodiment Discourses comprise perspectives on learning that refuse a separation of mental and physical. Mental and physical are understood as integrated and inseparable aspects of the body. Phrased differently, the body is not seen as something that a learner learns through, but as the learner. Correspondingly, behaviors are not seen as goals or indications of learning, but as integral elements of learning.

Universal Darwinism encompasses every extension of Darwin’s theory of evolution beyond its original application to biological organisms. Universal Darwinism is motivated by the conviction that such dynamics as variation, selection, and retention can be applied to other patterns. phenomena, and systems – such as those studied in psychology, sociology, medicine, computer science, and geology.

Embodied Cognition asserts that humans are doubly embodied. That is, human cognition simultaneously depends on having a biological body and being part of a socio-cultural corpus. These two nested bodies are intimately intertwined: one’s physical body defines possible movements and ranges of perception; the grander context in which one is embedded defines appropriate actions and the scopes of interpretive possibilities.

Machine Learning encompasses all efforts and strategies to structure and program machines to improve their performances on specific tasks. In these applications, “learning” is usually defined in operational rather than cognitive terms – that is, in terms of measurable improvements in performance that are due to changes in the machine’s structure, programs, or data. Many strategies are under development, employing different computer architectures and a range of logics.

Animal Cognition is concerned with observing and testing non-human animals’ cognitive capabilities. Foci include (but are not limited to) perception, attention, discrimination, categorization, training, rule learning, remembering, forgetting, anticipating, extrapolating, goal-seeking, perception of time, tool use, reasoning, problem solving, communication, language-like behavior, symbol use, sudden insight, quantity sense, and self-awareness.

Plant Cognition is a research domain concerned with experimentally testing plants’ capabilities to perceive, respond to, and learn from interactions and other experiences/stimuli. Some sub-interests include root interactions, plant spatial awareness, plant social systems, plants learning to anticipate through Classical Conditioning, plant decision-making triggered by environmental stresses, and possible similarities between plant structures and animal nervous systems.