Belief Revision in Dynamic Abducers through Meta-Abduction
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2010
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ndltd-OhioLink-oai-etd.ohiolink.edu-osu12767875092021-08-03T06:00:01Z Belief Revision in Dynamic Abducers through Meta-Abduction Bharathan, Vivek Artificial Intelligence Computer Science Artificial Intelligence Abductive Inference Belief Revision Entity Tracking <p>Abduction machines (or abducers) infer to the best explanation for the data presented to them, and may accumulate beliefs (the conclusions of the inferences) about the world. An abducer’s beliefs are justified as being the best explanation in contrast with alternative hypotheses. However, the best explanation available to (achievable by) an abducer need not be the true explanation for a variety of reasons including: incomplete search for alternative explanations, insufficient data, and inadequate background knowledge for evaluating alternatives. In light of this fallibility, algorithms were investigated for detecting and correcting errors, for dynamic abducers, by comparing a range of alternative algorithms with regard to effectiveness and computational costs. Dynamic abducers interpret an incoming information stream by producing their best explanations at any point in time. They accumulate new beliefs, and update older beliefs, in the light of new information.</p><p>In the present work, algorithms were developed for detecting and correcting errors in the accumulated beliefs of such abducers. These algorithms treat the problem of identifying errors as meta-abduction, where certain anomalies that occur during processing are explained as resulting from specific mistakes in previous abductive processing. Errors are then corrected, and beliefs revised, by adopting alternative explanations. A brute-force algorithm for this meta-abduction is computationally intractable, so heuristics were developed with prospects of improving performance. Since a priori mathematical analysis of the algorithms using these heuristics did not reveal useful bounds, simulation experiments were conducted, using a specimen domain. The domain was that of multi-object tracking, where an abduction machine, over time, attempts to maintain the track history of mobile entities, based on sensor reports. The experimental results suggest that, using only the heuristics that were investigated, belief revision by meta-abduction enables only small improvements in correctness, and is computationally expensive.</p> 2010-09-14 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1276787509 http://rave.ohiolink.edu/etdc/view?acc_num=osu1276787509 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
| collection |
NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
Artificial Intelligence Computer Science Artificial Intelligence Abductive Inference Belief Revision Entity Tracking |
| spellingShingle |
Artificial Intelligence Computer Science Artificial Intelligence Abductive Inference Belief Revision Entity Tracking Bharathan, Vivek Belief Revision in Dynamic Abducers through Meta-Abduction |
| author |
Bharathan, Vivek |
| author_facet |
Bharathan, Vivek |
| author_sort |
Bharathan, Vivek |
| title |
Belief Revision in Dynamic Abducers through Meta-Abduction |
| title_short |
Belief Revision in Dynamic Abducers through Meta-Abduction |
| title_full |
Belief Revision in Dynamic Abducers through Meta-Abduction |
| title_fullStr |
Belief Revision in Dynamic Abducers through Meta-Abduction |
| title_full_unstemmed |
Belief Revision in Dynamic Abducers through Meta-Abduction |
| title_sort |
belief revision in dynamic abducers through meta-abduction |
| publisher |
The Ohio State University / OhioLINK |
| publishDate |
2010 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=osu1276787509 |
| work_keys_str_mv |
AT bharathanvivek beliefrevisionindynamicabducersthroughmetaabduction |
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1719429158523633664 |