17,541 research outputs found
Retrieval, reuse, revision and retention in case-based reasoning
El original está disponible en www.journals.cambridge.orgCase-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if
necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief
overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision, and retention.Peer reviewe
Experiments in reactive constraint logic programming1This paper is the complete version of a previous paper published in [14].1
AbstractIn this paper we study a reactive extension of constraint logic programming (CLP). Our primary concerns are search problems in a dynamic environment, where interactions with the user (e.g. in interactive multi-criteria optimization problems) or interactions with the physical world (e.g. in time evolving problems) can be modeled and solved efficiently. Our approach is based on a complete set of query manipulation commands for both the addition and the deletion of constraints and atoms in the query. We define a fully incremental model of execution which, contrary to other proposals, retains as much information as possible from the last derivation preceding a query manipulation command. The completeness of the execution model is proved in a simple framework of transformations for CSLD derivations, and of constraint propagation seen as chaotic iteration of closure operators. A prototype implementation of this execution model is described and evaluated on two applications
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
Generating Weather Forecast Texts with Case Based Reasoning
Several techniques have been used to generate weather forecast texts. In this
paper, case based reasoning (CBR) is proposed for weather forecast text
generation because similar weather conditions occur over time and should have
similar forecast texts. CBR-METEO, a system for generating weather forecast
texts was developed using a generic framework (jCOLIBRI) which provides modules
for the standard components of the CBR architecture. The advantage in a CBR
approach is that systems can be built in minimal time with far less human
effort after initial consultation with experts. The approach depends heavily on
the goodness of the retrieval and revision components of the CBR process. We
evaluated CBRMETEO with NIST, an automated metric which has been shown to
correlate well with human judgements for this domain. The system shows
comparable performance with other NLG systems that perform the same task.Comment: 6 page
The positive impacts of interactive whiteboards on student learning outcomes in FE colleges, and the conditions under which outcomes can be maximised.
This paper draws from a wider study on the use and impact of ICT within FE colleges. The research questions addressed are: what is it about the ways interactive whiteboards (iWBs) are being used that produce positive impacts on student outcomes, and what institutional and personal factors determine which teachers use iWBs effectively? Multiple case-studies of 6 colleges were designed using a new framework for classifying e-learning uses (ELUs) according to the learning context, learning objectives and the types of software and activities being used. Tutors’ beliefs in the efficacy of iWB use, their intentions for use, teaching style and pedagogical skills, and the subject taught all affected the ways in which iWB were deployed, and in particular the degree of multimedia and pedagogic interactivity. Tutors who made a lot of use of iWBs were in colleges where the leadership vision prioritised ICT within teaching and learning. The strongest impact on student outcomes occurred where iWBs were used in a variety of ways, use was appropriate for the subject, and congruent with the teachers' purposes and intentions for students' learning. Tutors who made little use of iWBs tended to be in colleges where the emphasis on management of learning was stronger than on supporting pedagogic development, and/or they were unaware of the potential of iWBs particularly in relation to their subject
Quantum Optimization Problems
Krentel [J. Comput. System. Sci., 36, pp.490--509] presented a framework for
an NP optimization problem that searches an optimal value among
exponentially-many outcomes of polynomial-time computations. This paper expands
his framework to a quantum optimization problem using polynomial-time quantum
computations and introduces the notion of an ``universal'' quantum optimization
problem similar to a classical ``complete'' optimization problem. We exhibit a
canonical quantum optimization problem that is universal for the class of
polynomial-time quantum optimization problems. We show in a certain relativized
world that all quantum optimization problems cannot be approximated closely by
quantum polynomial-time computations. We also study the complexity of quantum
optimization problems in connection to well-known complexity classes.Comment: date change
Evaluating Human-Language Model Interaction
Many real-world applications of language models (LMs), such as writing
assistance and code autocomplete, involve human-LM interaction. However, most
benchmarks are non-interactive in that a model produces output without human
involvement. To evaluate human-LM interaction, we develop a new framework,
Human-AI Language-based Interaction Evaluation (HALIE), that defines the
components of interactive systems and dimensions to consider when designing
evaluation metrics. Compared to standard, non-interactive evaluation, HALIE
captures (i) the interactive process, not only the final output; (ii) the
first-person subjective experience, not just a third-party assessment; and
(iii) notions of preference beyond quality (e.g., enjoyment and ownership). We
then design five tasks to cover different forms of interaction: social
dialogue, question answering, crossword puzzles, summarization, and metaphor
generation. With four state-of-the-art LMs (three variants of OpenAI's GPT-3
and AI21 Labs' Jurassic-1), we find that better non-interactive performance
does not always translate to better human-LM interaction. In particular, we
highlight three cases where the results from non-interactive and interactive
metrics diverge and underscore the importance of human-LM interaction for LM
evaluation.Comment: Authored by the Center for Research on Foundation Models (CRFM) at
the Stanford Institute for Human-Centered Artificial Intelligence (HAI
Logical and uncertainty models for information access: current trends
The current trends of research in information access as emerged from the 1999 Workshop on Logical and Uncertainty Models for Information Systems (LUMIS'99) are briefly reviewed in this paper. We believe that some of these issues will be central to future research on theory and applications of logical and uncertainty models for information access
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