Biological concept formation grammars: A flexible, multiagent linguistic tool for biological processes

Constraint based models that are useful for processing biological information have been successfully put forward recently, e.g. for representing multi-disciplinary biological knowledge in view of cancer diagnosis, and for reconstructing RNA sequences from secondary structure. Here we generalize such results into a model for biological concept formation which can interact with heterogeneous agents through constraint-based reasoning. Our model includes linguistic agents, probabilistic agents for mining nucleic acid, and illness diagnosis agents. Information is selected automatically as a side effect of (the system) searching through applicable CHR rules, and automatically transformed when a rule triggers; decisions follow from the normal operation of the rules, and cognitive structure is given by properties that the concepts a given rule is trying to relate must satisfy. Moreover the user can declare under what circumstances a given property or properties can be relaxed. Concepts formed under relaxed properties result in output which signals not only what concepts were formed, but which of the properties associated with the construction of those concepts were satisfied and which were not. This allows us human-like flexibility while maintaining direct executability.