Laws@SRI-AI.ARPA (12/14/83)
From: Ken Laws <Laws@SRI-AI.ARPA>
Two articles in the Nov. issue of CACM (just arrived) may be of
special interest to AI researchers:
"Maintaining Knowledge about Temporal Intervals," by James F. Allen
of the U. of Rochester, is about representation of temporal information
using only intervals -- no points. While this work does not lead to a
fully general temporal calculus, it goes well beyond state space and
date line systems and is more powerful and efficient than event chaining
representations. I can imagine that the approach could be generalized
to higher dimensions, e.g., for reasoning about the relationships of
image regions or objects in the 3-D world.
"Extended Boolean Information Retrieval," by Gerald Salton, Edward A. Fox,
and Harry Wu, presents a fuzzy logic or hierarchical inference method for
dealing with uncertainties when evaluating logical formulas. In a
formula such as ((A and B) or (B and C)), they present evidential
combining formulas that allow for:
* Uncertainty in the truth, reliability, or applicability of the
the primitive terms A and B;
* Differing importance of establishing the primitive term instances
(where the two B terms above could be weighted differently);
* Differing semantics of the logical connectives (where the two
"and" connectives above could be threshold units with different
thresholds).
The output of their formula evaluator is a numerical score. They use
this for ranking the pertinence of literature citations to a database
query, but it could also be used for evidential reasoning or for
evaluating possible worlds in a planning system. For the database
query system, they indicate a method for determining term weights
automatically from an inverted index of the database.
The weighting of the Boolean connectives is based on the infinite set
of Lp vector norms. The connectives and[INF] and or[INF] are the
ones of standard logic; and[1] and or[1] are equivalent and reduce
formula evaluation to a simple weighted summation; intermediate
connective norms correspond to "mostly" gates or weighted neural
logic models. The authors present both graphical illustrations and
logical theorems about these connectives.
-- Ken Laws