7,103 research outputs found
What are natural concepts? A design perspective
Conceptual spaces have become an increasingly popular modeling tool in cognitive psychology. The core idea of the conceptual spaces approach is that concepts can be represented as regions in similarity spaces. While it is generally acknowledged that not every region in such a space represents a natural concept, it is still an open question what distinguishes those regions that represent natural concepts from those that do not. The central claim of this paper is that natural concepts are represented by the cells of an optimally designed similarity space
From Frequency to Meaning: Vector Space Models of Semantics
Computers understand very little of the meaning of human language. This
profoundly limits our ability to give instructions to computers, the ability of
computers to explain their actions to us, and the ability of computers to
analyse and process text. Vector space models (VSMs) of semantics are beginning
to address these limits. This paper surveys the use of VSMs for semantic
processing of text. We organize the literature on VSMs according to the
structure of the matrix in a VSM. There are currently three broad classes of
VSMs, based on term-document, word-context, and pair-pattern matrices, yielding
three classes of applications. We survey a broad range of applications in these
three categories and we take a detailed look at a specific open source project
in each category. Our goal in this survey is to show the breadth of
applications of VSMs for semantics, to provide a new perspective on VSMs for
those who are already familiar with the area, and to provide pointers into the
literature for those who are less familiar with the field
A Benchmark Environment Motivated by Industrial Control Problems
In the research area of reinforcement learning (RL), frequently novel and
promising methods are developed and introduced to the RL community. However,
although many researchers are keen to apply their methods on real-world
problems, implementing such methods in real industry environments often is a
frustrating and tedious process. Generally, academic research groups have only
limited access to real industrial data and applications. For this reason, new
methods are usually developed, evaluated and compared by using artificial
software benchmarks. On one hand, these benchmarks are designed to provide
interpretable RL training scenarios and detailed insight into the learning
process of the method on hand. On the other hand, they usually do not share
much similarity with industrial real-world applications. For this reason we
used our industry experience to design a benchmark which bridges the gap
between freely available, documented, and motivated artificial benchmarks and
properties of real industrial problems. The resulting industrial benchmark (IB)
has been made publicly available to the RL community by publishing its Java and
Python code, including an OpenAI Gym wrapper, on Github. In this paper we
motivate and describe in detail the IB's dynamics and identify prototypic
experimental settings that capture common situations in real-world industry
control problems
A Benchmark Environment Motivated by Industrial Control Problems
In the research area of reinforcement learning (RL), frequently novel and
promising methods are developed and introduced to the RL community. However,
although many researchers are keen to apply their methods on real-world
problems, implementing such methods in real industry environments often is a
frustrating and tedious process. Generally, academic research groups have only
limited access to real industrial data and applications. For this reason, new
methods are usually developed, evaluated and compared by using artificial
software benchmarks. On one hand, these benchmarks are designed to provide
interpretable RL training scenarios and detailed insight into the learning
process of the method on hand. On the other hand, they usually do not share
much similarity with industrial real-world applications. For this reason we
used our industry experience to design a benchmark which bridges the gap
between freely available, documented, and motivated artificial benchmarks and
properties of real industrial problems. The resulting industrial benchmark (IB)
has been made publicly available to the RL community by publishing its Java and
Python code, including an OpenAI Gym wrapper, on Github. In this paper we
motivate and describe in detail the IB's dynamics and identify prototypic
experimental settings that capture common situations in real-world industry
control problems
KNOWLEDGE SHARING AND NEGOTIATION SUPPORT IN MULTIPERSON DECISION SUPPORT SYSTEMS
A number of DSS for supporting decisions by more than one person have been
proposed. These can be categorized by spatial distance (local vs. remote),
temporal distance (meeting vs. mailing), commonality of goals (cooperation
vs. bargaining), and control (democratic vs. hierarchical). Existing
frameworks for model management in single-user DSS seem insufficient for
such systems.
This paper views multiperson DSS as a loosely coupled system of model and
data bases which may be human (the DSS builders and users) or computerized.
The systems components have different knowledge bases and may have
different interests. Their interaction is characterized by knowledge
sharing for uncertainty reduction and cooperative problem-solving, and
negotiation for view integration, consensus-seeking, and compromise.
Requirements for the different types of multiperson DSS can be formalized
as application-level communications protocols. Based on a literature
review and recent experience with a number of multiperson DSS prototypes,
artificial intelligence-based message-passing protocols are compared with
database-centered approaches and model-based techniques, such as
multicriteria decision making.Information Systems Working Papers Serie
Improving Transfer of Learning Through Analogical Thinking
This project focused on developing a method for teaching creative thinking tools in ways that enable learning transfer. In the process of defining and identifying stimulants and obstacles for learning transfer, the literature revealed that analogical thinking, a long-standing creative thinking mechanism, is analogous to learning transfer. Many cognitive psychology researchers suggest that since humans can only describe new concepts in terms of things that are already understood, analogical thinking is the basis for all learning. “It is not our senses that limit or liberate us, but our ability to illuminate the unknown by means of analogies to the known. Learning itself depends on analogizing.” (Root-Bernstein & Root-Bernstein, 1999, p.142). A review of the literature on analogical thinking revealed common process steps for using analogical models in teaching. Accelerated learning concepts and components of the Torrance Incubation Model are used to outline a module for teaching analogical thinking. The use of concept maps for the structure mapping step of analogical thinking is recommended
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