1,022 research outputs found
Systematicity and surface similarity in the development of analogy
In split page format (number of pages: 45)Includes bibliographical reference
An extended abstract: A heuristic repair method for constraint-satisfaction and scheduling problems
The work described in this paper was inspired by a surprisingly effective neural network developed for scheduling astronomical observations on the Hubble Space Telescope. Our heuristic constraint satisfaction problem (CSP) method was distilled from an analysis of the network. In the process of carrying out the analysis, we discovered that the effectiveness of the network has little to do with its connectionist implementation. Furthermore, the ideas employed in the network can be implemented very efficiently within a symbolic CSP framework. The symbolic implementation is extremely simple. It also has the advantage that several different search strategies can be employed, although we have found that hill-climbing methods are particularly well-suited for the applications that we have investigated. We begin the paper with a brief review of the neural network. Following this, we describe our symbolic method for heuristic repair
Probabilistic Analogical Mapping with Semantic Relation Networks
The human ability to flexibly reason using analogies with domain-general
content depends on mechanisms for identifying relations between concepts, and
for mapping concepts and their relations across analogs. Building on a recent
model of how semantic relations can be learned from non-relational word
embeddings, we present a new computational model of mapping between two
analogs. The model adopts a Bayesian framework for probabilistic graph
matching, operating on semantic relation networks constructed from distributed
representations of individual concepts and of relations between concepts.
Through comparisons of model predictions with human performance in a novel
mapping task requiring integration of multiple relations, as well as in several
classic studies, we demonstrate that the model accounts for a broad range of
phenomena involving analogical mapping by both adults and children. We also
show the potential for extending the model to deal with analog retrieval. Our
approach demonstrates that human-like analogical mapping can emerge from
comparison mechanisms applied to rich semantic representations of individual
concepts and relations
Internet-based medical education: a realist review of what works, for whom and in what circumstances
http://creativecommons.org/licenses/by/2.0
On Improving Local Search for Unsatisfiability
Stochastic local search (SLS) has been an active field of research in the
last few years, with new techniques and procedures being developed at an
astonishing rate. SLS has been traditionally associated with satisfiability
solving, that is, finding a solution for a given problem instance, as its
intrinsic nature does not address unsatisfiable problems. Unsatisfiable
instances were therefore commonly solved using backtrack search solvers. For
this reason, in the late 90s Selman, Kautz and McAllester proposed a challenge
to use local search instead to prove unsatisfiability. More recently, two SLS
solvers - Ranger and Gunsat - have been developed, which are able to prove
unsatisfiability albeit being SLS solvers. In this paper, we first compare
Ranger with Gunsat and then propose to improve Ranger performance using some of
Gunsat's techniques, namely unit propagation look-ahead and extended
resolution
Adolescent Literacy and Textbooks: An Annotated Bibliography
A companion report to Carnegie's Time to Act, provides an annotated bibliography of research on textbook design and reading comprehension for fourth through twelfth grade, arranged by topic. Calls for a dialogue between publishers and researchers
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