50 research outputs found
The aggregation of propositional attitudes: towards a general theory
How can the propositional attitudes of several individuals be aggregated into overall collective propositional attitudes? Although there are large bodies of work on the aggregation of various special kinds of propositional attitudes, such as preferences, judgments, probabilities and utilities, the aggregation of propositional attitudes is seldom studied in full generality. In this paper, we seek to contribute to filling this gap in the literature. We sketch the ingredients of a general theory of propositional attitude aggregation and prove two new theorems. Our first theorem simultaneously characterizes some prominent aggregation rules in the cases of probability, judgment and preference aggregation, including linear opinion pooling and Arrovian dictatorships. Our second theorem abstracts even further from the specific kinds of attitudes in question and describes the properties of a large class of aggregation rules applicable to a variety of belief-like attitudes. Our approach integrates some previously disconnected areas of investigation.mathematical economics;
Smoothing and filtering with a class of outer measures
Filtering and smoothing with a generalised representation of uncertainty is
considered. Here, uncertainty is represented using a class of outer measures.
It is shown how this representation of uncertainty can be propagated using
outer-measure-type versions of Markov kernels and generalised Bayesian-like
update equations. This leads to a system of generalised smoothing and filtering
equations where integrals are replaced by supremums and probability density
functions are replaced by positive functions with supremum equal to one.
Interestingly, these equations retain most of the structure found in the
classical Bayesian filtering framework. It is additionally shown that the
Kalman filter recursion can be recovered from weaker assumptions on the
available information on the corresponding hidden Markov model
A Human-Centric Approach to Group-Based Context-Awareness
The emerging need for qualitative approaches in context-aware information
processing calls for proper modeling of context information and efficient
handling of its inherent uncertainty resulted from human interpretation and
usage. Many of the current approaches to context-awareness either lack a solid
theoretical basis for modeling or ignore important requirements such as
modularity, high-order uncertainty management and group-based
context-awareness. Therefore, their real-world application and extendability
remains limited. In this paper, we present f-Context as a service-based
context-awareness framework, based on language-action perspective (LAP) theory
for modeling. Then we identify some of the complex, informational parts of
context which contain high-order uncertainties due to differences between
members of the group in defining them. An agent-based perceptual computer
architecture is proposed for implementing f-Context that uses computing with
words (CWW) for handling uncertainty. The feasibility of f-Context is analyzed
using a realistic scenario involving a group of mobile users. We believe that
the proposed approach can open the door to future research on context-awareness
by offering a theoretical foundation based on human communication, and a
service-based layered architecture which exploits CWW for context-aware,
group-based and platform-independent access to information systems
Missing data treatment and data fusion toward travel time estimation for ATIS
[[abstract]]This study develops a travel time estimation process by integrating a missing data treatment and data-fusion-based approaches. In missing data treatment, this study develops a grey time-series model and a grey-theory-based pseudo-nearest-neighbor method to recover, respectively, temporal and spatial missing values in traffic detector data sets. Both spatial and temporal patterns of traffic data are also considered in travel time data fusion. In travel time data fusion, this study presents a speed-based link travel time extrapolation model for analytical travel time estimation and further develops a recurrent neural network (RNN) integrated with grey models for real-time travel time estimation. In the case study, field data from the national freeway no. 1 in Taiwan is used as a case study for testing the proposed models. Study results showed that the grey-theory-based missing data treatment models were accurate for recovering missing values. The grey-based RNN models were capable of accurately predicting travel times. Consequently, the results of this study indicated that the proposed missing data treatment and data fusion approaches can ensure the accuracy of travel time estimation with incomplete data sets, and are therefore suited to implementation for ATIS.[[notice]]補正完
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Expert systems for management of pests of agricultural crops
Expert advisory systems for agricultural pest management control offer the means to capitalize on the wealth of information that is currently tied up in research laboratories and human experts' minds. The ideal system would blend knowledge from three sources - human experts, dynamic simulation models, and historical databases - to identify pests and to produce advisories for their management. We describe the design of such a system and the progress to date in the construction of prototype systems