|A self-adaptive architecture for interpretation problems|
|Affiliation||MIT AI Lab|
Much of A.I. is concerned with interpretation problems. Vision programs
are concerned with interpreting visual scenes, speech understanding systems
are concerned with interpreting spoken words, natural language systems are
concerned with interpreting strings of words, and robot controllers are
concerned with interpreting the landscape through which the robot must navigate.
Programs that learn are concerned with interpretating data as a model, programs
that generate code interpret specifications, and programs that follow a
plan interpret the plan as a sequence of actions. Interpretation as a general
concept is useful in building systems that are involved with understanding
Building systems that can interact intelligently with a complex environment requires complex programs. Over the years numerous architectures have been developed to facilitate the problem of building complex systems that exhibit interesting bahavior in the face of a complex environment. Examples of such architectures include Blackboards, Forward chaining rule based systems, Schemas, and subsumption.
I will describe a self-adaptive reflective architecture for interpretation problems that has been applied to the problem of interpreting satelite images. I will not describe any of the vision algorithms but will focus on the details of the architecture and how it relates to other approaches.
|Location||545 Technology Square (aka "NE43")|
|Room||8th Floor Playroom|