118,486 research outputs found
Using Natural Language as Knowledge Representation in an Intelligent Tutoring System
Knowledge used in an intelligent tutoring system to teach students is usually acquired from authors who are experts in the domain. A problem is that they cannot directly add and update knowledge if they don’t learn formal language used in the system. Using natural language to represent knowledge can allow authors to update knowledge easily. This thesis presents a new approach to use unconstrained natural language as knowledge representation for a physics tutoring system so that non-programmers can add knowledge without learning a new knowledge representation. This approach allows domain experts to add not only problem statements, but also background knowledge such as commonsense and domain knowledge including principles in natural language. Rather than translating into a formal language, natural language representation is directly used in inference so that domain experts can understand the internal process, detect knowledge bugs, and revise the knowledgebase easily. In authoring task studies with the new system based on this approach, it was shown that the size of added knowledge was small enough for a domain expert to add, and converged to near zero as more problems were added in one mental model test. After entering the no-new-knowledge state in the test, 5 out of 13 problems (38 percent) were automatically solved by the system without adding new knowledge
An Introduction to Rule-based Modeling of Immune Receptor Signaling
Cells process external and internal signals through chemical interactions.
Cells that constitute the immune system (e.g., antigen presenting cell, T-cell,
B-cell, mast cell) can have different functions (e.g., adaptive memory,
inflammatory response) depending on the type and number of receptor molecules
on the cell surface and the specific intracellular signaling pathways activated
by those receptors. Explicitly modeling and simulating kinetic interactions
between molecules allows us to pose questions about the dynamics of a signaling
network under various conditions. However, the application of chemical kinetics
to biochemical signaling systems has been limited by the complexity of the
systems under consideration. Rule-based modeling (BioNetGen, Kappa, Simmune,
PySB) is an approach to address this complexity. In this chapter, by
application to the FcRI receptor system, we will explore the
origins of complexity in macromolecular interactions, show how rule-based
modeling can be used to address complexity, and demonstrate how to build a
model in the BioNetGen framework. Open source BioNetGen software and
documentation are available at http://bionetgen.org.Comment: 5 figure
Constraint Handling Rules with Binders, Patterns and Generic Quantification
Constraint Handling Rules provide descriptions for constraint solvers.
However, they fall short when those constraints specify some binding structure,
like higher-rank types in a constraint-based type inference algorithm. In this
paper, the term syntax of constraints is replaced by -tree syntax, in
which binding is explicit; and a new generic quantifier is introduced,
which is used to create new fresh constants.Comment: Paper presented at the 33nd International Conference on Logic
Programming (ICLP 2017), Melbourne, Australia, August 28 to September 1, 2017
16 pages, LaTeX, no PDF figure
Distributed associative memories for high-speed symbolic reasoning
This paper briefly introduces a novel symbolic reasoning system based upon distributed associative memories which are constructed from correlation matrix memories (CMM). The system is aimed at high-speed rule-based symbolic operations. It has the advantage of very fast rule matching without the long training times normally associated with neural-network-based symbolic manipulation systems. In particular, the network is able to perform partial matching on symbolic information at high speed. As such, the system is aimed at the practical use of neural networks in high-speed reasoning systems. The paper describes the advantages and disadvantages of using CMM and shows how the approach overcomes those disadvantages. It then briefly describes a system incorporating CMM
Inferring Chemical Reaction Patterns Using Rule Composition in Graph Grammars
Modeling molecules as undirected graphs and chemical reactions as graph
rewriting operations is a natural and convenient approach tom odeling
chemistry. Graph grammar rules are most naturally employed to model elementary
reactions like merging, splitting, and isomerisation of molecules. It is often
convenient, in particular in the analysis of larger systems, to summarize
several subsequent reactions into a single composite chemical reaction. We use
a generic approach for composing graph grammar rules to define a chemically
useful rule compositions. We iteratively apply these rule compositions to
elementary transformations in order to automatically infer complex
transformation patterns. This is useful for instance to understand the net
effect of complex catalytic cycles such as the Formose reaction. The
automatically inferred graph grammar rule is a generic representative that also
covers the overall reaction pattern of the Formose cycle, namely two carbonyl
groups that can react with a bound glycolaldehyde to a second glycolaldehyde.
Rule composition also can be used to study polymerization reactions as well as
more complicated iterative reaction schemes. Terpenes and the polyketides, for
instance, form two naturally occurring classes of compounds of utmost
pharmaceutical interest that can be understood as "generalized polymers"
consisting of five-carbon (isoprene) and two-carbon units, respectively
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