Thinking Between the Lines targets a challenge at the heart of the artificial intelligence enterprise: the design of programs that can read and reason on the basis of written causal descriptions such as those that appear in encyclopedias, user manuals, and related sources. This capability of "thinking between the lines" -- codified in terms of a task called "causal reconstruction" -- bears directly on the larger question of how computers can usefully exploit the vast repertory of human knowledge concerning causal phenomena.Central to the approach presented is a cognitively inspired representation called "transition space," implemented in a program called PATHFINDER. The transition space representation embodies a conceptual shift from viewing the world primarily in terms of states -- or instantaneous snapshots of activity -- to viewing it primarily in terms of transitions -- ensembles of changes that can be articulated in language. Transitions, according to this view, serve as antecedents and consequents of causality, and the space of all possible transitions -- or transition space -- serves as an arena for working out paths of association between the events mentioned within particular causal descriptions.Thinking between the Lines provides a computational framework and approach for realizing the significant opportunities that arise for intelligent, automated handling of technical material -- in routing information, answering questions, elaborating or summarizing information to meet the needs of particular individuals, and performing other useful tasks.Artificial Intelligence series
Thinking Between the Lines targets a challenge at the heart of the artificial intelligence enterprise: the design of programs that can read and reason on the basis of written causal descriptions such as those that appear in encyclopedias, user manuals, and related sources. This capability of "thinking between the lines" -- codified in terms of a task called "causal reconstruction" -- bears directly on the larger question of how computers can usefully exploit the vast repertory of human knowledge concerning causal phenomena.Central to the approach presented is a cognitively inspired representation called "transition space," implemented in a program called PATHFINDER. The transition space representation embodies a conceptual shift from viewing the world primarily in terms of states -- or instantaneous snapshots of activity -- to viewing it primarily in terms of transitions -- ensembles of changes that can be articulated in language. Transitions, according to this view, serve as antecedents and consequents of causality, and the space of all possible transitions -- or transition space -- serves as an arena for working out paths of association between the events mentioned within particular causal descriptions.Thinking between the Lines provides a computational framework and approach for realizing the significant opportunities that arise for intelligent, automated handling of technical material -- in routing information, answering questions, elaborating or summarizing information to meet the needs of particular individuals, and performing other useful tasks.Artificial Intelligence series
