168 research outputs found
An Expressive Language and Efficient Execution System for Software Agents
Software agents can be used to automate many of the tedious, time-consuming
information processing tasks that humans currently have to complete manually.
However, to do so, agent plans must be capable of representing the myriad of
actions and control flows required to perform those tasks. In addition, since
these tasks can require integrating multiple sources of remote information ?
typically, a slow, I/O-bound process ? it is desirable to make execution as
efficient as possible. To address both of these needs, we present a flexible
software agent plan language and a highly parallel execution system that enable
the efficient execution of expressive agent plans. The plan language allows
complex tasks to be more easily expressed by providing a variety of operators
for flexibly processing the data as well as supporting subplans (for
modularity) and recursion (for indeterminate looping). The executor is based on
a streaming dataflow model of execution to maximize the amount of operator and
data parallelism possible at runtime. We have implemented both the language and
executor in a system called THESEUS. Our results from testing THESEUS show that
streaming dataflow execution can yield significant speedups over both
traditional serial (von Neumann) as well as non-streaming dataflow-style
execution that existing software and robot agent execution systems currently
support. In addition, we show how plans written in the language we present can
represent certain types of subtasks that cannot be accomplished using the
languages supported by network query engines. Finally, we demonstrate that the
increased expressivity of our plan language does not hamper performance;
specifically, we show how data can be integrated from multiple remote sources
just as efficiently using our architecture as is possible with a
state-of-the-art streaming-dataflow network query engine
Wrapper Maintenance: A Machine Learning Approach
The proliferation of online information sources has led to an increased use
of wrappers for extracting data from Web sources. While most of the previous
research has focused on quick and efficient generation of wrappers, the
development of tools for wrapper maintenance has received less attention. This
is an important research problem because Web sources often change in ways that
prevent the wrappers from extracting data correctly. We present an efficient
algorithm that learns structural information about data from positive examples
alone. We describe how this information can be used for two wrapper maintenance
applications: wrapper verification and reinduction. The wrapper verification
system detects when a wrapper is not extracting correct data, usually because
the Web source has changed its format. The reinduction algorithm automatically
recovers from changes in the Web source by identifying data on Web pages so
that a new wrapper may be generated for this source. To validate our approach,
we monitored 27 wrappers over a period of a year. The verification algorithm
correctly discovered 35 of the 37 wrapper changes, and made 16 mistakes,
resulting in precision of 0.73 and recall of 0.95. We validated the reinduction
algorithm on ten Web sources. We were able to successfully reinduce the
wrappers, obtaining precision and recall values of 0.90 and 0.80 on the data
extraction task
Active Learning with Multiple Views
Active learners alleviate the burden of labeling large amounts of data by
detecting and asking the user to label only the most informative examples in
the domain. We focus here on active learning for multi-view domains, in which
there are several disjoint subsets of features (views), each of which is
sufficient to learn the target concept. In this paper we make several
contributions. First, we introduce Co-Testing, which is the first approach to
multi-view active learning. Second, we extend the multi-view learning framework
by also exploiting weak views, which are adequate only for learning a concept
that is more general/specific than the target concept. Finally, we empirically
show that Co-Testing outperforms existing active learners on a variety of real
world domains such as wrapper induction, Web page classification, advertisement
removal, and discourse tree parsing
The effects of exposure to a rotating environment /10 rpm/ on four aviators for a period of twelve days
Motion sickness studies of aviators exposed to rotating environment - Aerospace medicin
Archaeology and Language: The Indo-Iranians
This review of recent archaeological work in Central Asia and Eurasia attempts to trace and date the movements of the IndoIraniansspeakers of languages of the eastern branch of ProtoIndoEuropean that later split into the Iranian and Vedic families. Russian and Central Asian scholars working on the contemporary but very different Andronovo and Bactrian Margiana archaeological complexes of the 2d millennium b.c. have identified both as IndoIranian, and particular sites so identified are being used for nationalist purposes. There is, however, no compelling archaeological evidence that they had a common ancestor or that either is IndoIranian. Ethnicity and language are not easily linked with an archaeological signature, and the identity of the IndoIranians remains elusive
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