Aaron Kaplan's Publications
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Aaron N. Kaplan. Towards a Consistent
Logical Framework for Ontological Analysis. In Proceedings of
the International Conference on Formal Ontology in Information
Systems, Ogunquit, Maine, October, 2001.
In their framework for ontological analysis, Guarino and Welty
provide a number of insights that are useful for guiding the design
of taxonomic hierarchies. However, the formal statements of these
insights as logical schemata are flawed in a number of ways,
including inconsistent notation that makes the intended semantics of
the logic unclear, false claims of logical consequence, and
definitions that provably result in the triviality of some of their
property features. This paper makes a negative contribution, by
demonstrating these flaws in a rigorous way, but also makes a
positive contribution wherever possible, by identifying the
underlying intuitions that the faulty definitions were intended to
capture, and attempting to formalize those intuitions in a more
accurate way.
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Aaron N. Kaplan. A Computational Model of
Belief. PhD thesis, University of Rochester, 2000.
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Aaron N. Kaplan and Lenhart K. Schubert.
A computational model of belief. Artificial
Intelligence, 120:119-160, 2000.
We propose a logic of belief in which the expansion of beliefs
beyond what has been explicitly learned is modeled as a finite
computational process. The logic does not impose a particular
computational mechanism; rather, the mechanism is a parameter of the
logic, and we show that as long as the mechanism meets a particular
set of constraints, the resulting logic has certain desirable
properties. Chief among these is the property that one can reason
soundly about another agent's beliefs by simulating its
computational mechanism with one's own. We also give a detailed
comparison of our model with Konolige's deduction model, another
model of belief in which the believer's reasoning mechanism is a
parameter.
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Aaron N. Kaplan. Reason maintenance in a hybrid
reasoning system.
Journal of Language and Computation, 1(2):247-259, 2000.
Preliminary version presented at Workshop on Inference in
Computational Semantics, Amsterdam, August 1999.
One can gain efficiency in an inference system by using
special-purpose representations for reasoning about certain
predicates, but some such representations make it impossible for the
system to keep track of the reasons for which it holds each of its
beliefs. We illustrate the potential conflict with some examples,
and distill some general principles for designing representations
that can support both efficient special-purpose inference and some
form of reason maintenance.
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Aaron N. Kaplan and Lenhart K. Schubert. Simulative inference in a
computational model of belief. In Harry Bunt and Reinhard Muskens,
editors, Computing Meaning, Studies in Linguistics and
Philosophy series, pages 185-202. Kluwer, 1999. Preliminary version
presented at International Workshop on Computational Semantics,
Tilburg, The Netherlands, January 1997.
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Aaron N. Kaplan (1998). ``Simulative Inference
About Nonmonotonic Reasoners.'' Theoretical Aspects of
Rationality and Knowledge: Proceedings of the Seventh Conference,
pages 71-81.
If one has attributed certain initial beliefs to an agent, it is
sometimes possible to reason about further beliefs the agent must
hold by observing what conclusions one's own reasoning mechanism
draws when given the initial beliefs as premises. This technique is
called simulative inference. In an earlier paper, we described a
logic of belief in which the reasoning that generates beliefs is
modeled explicitly as a computational process. We used this logic
to characterize a class of computational inference mechanisms for
which simulative inference is sound, under the assumption that the
observer and the observed have similar mechanisms. In this paper,
we present a different form of simulative inference, and show that
unlike the earlier form, it is sound even for some mechanisms that
perform defeasible inference.
Aaron
Kaplan's home page
Research in CS at U of R