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You searched for +publisher:"University of Southern California" +contributor:("Raghavendra, Raghu"). Showing records 1 – 2 of 2 total matches.

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University of Southern California

1. Sun, Xiaoxun. Incremental search-based path planning for moving target search.

Degree: PhD, Computer Science, 2013, University of Southern California

In this dissertation, I demonstrate how to speed up path planning for moving target search, which is a problem where an agent needs to move to a target and the target can move over time. It is assumed that the current locations of the agent and the target are known to the agent at all times. The information about the terrain that an agent has is the action costs of the agent in any particular location of the terrain. The information about the terrain that the agent has can change over time depending on different applications: For example, when a robot is deployed in a new terrain without any map a priori, the robot initially does not have any information about the terrain and it has to acquire information about the terrain during navigation. However, a character in a computer game may have complete information about the terrain that remains unchanged over time given that the whole game map is loaded in memory and is available to the character. I use the following movement strategy for the agent that is based on assumptive planning: The agent first finds a cost-minimal path to the target with the information about the terrain that is currently available to it. The agent then starts moving along the path. Whenever new information about the terrain is acquired or the target moves off the path, the agent performs a new search to find a new cost-minimal path from the agent to the target. The agent uses this movement strategy until either the target is caught or the agent finds that there does not exist any path from the agent to the target after a search (and in any future searches), upon which the agent stops navigation. Since the agent's information about the terrain can change and the target can move over time, the agent needs to repeatedly perform searches to find new cost-minimal paths to the target. Path planning for moving target search by using this movement strategy is thus often a repeated search process. Additionally, agents need to find new cost-minimal paths as fast as possible, such that they move smoothly and without delay. ❧ Many path planning algorithms have been developed, among which incremental search algorithms reuse information from previous searches to speed up the current search and are thus often able to find cost-minimal paths for series of similar search problems faster than by solving each search problem from scratch. Incremental search algorithms have been demonstrated to be very successful in path planning for many important applications in robotics. However, it is assumed that the target does not move over time during navigation for most incremental search algorithms, and they are either inapplicable or run more slowly than A* to solve moving target search. Thus, I demonstrate how to speed up search-based path planning for moving target search by developing new incremental search algorithms. ❧ In my dissertation, I make the following contributions: (1) I develop Generalized Adaptive A* (GAA*), that learns h-values (= heuristic values) to make them more informed for moving target search.… Advisors/Committee Members: Koenig, Sven (Committee Chair), Likhachev, Maxim (Committee Member), Zyda, Michael (Committee Member), Raghavendra, Raghu (Committee Member).

Subjects/Keywords: artificial intelligence; search; heuristic search; moving target search

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sun, X. (2013). Incremental search-based path planning for moving target search. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/236875/rec/3456

Chicago Manual of Style (16th Edition):

Sun, Xiaoxun. “Incremental search-based path planning for moving target search.” 2013. Doctoral Dissertation, University of Southern California. Accessed November 21, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/236875/rec/3456.

MLA Handbook (7th Edition):

Sun, Xiaoxun. “Incremental search-based path planning for moving target search.” 2013. Web. 21 Nov 2019.

Vancouver:

Sun X. Incremental search-based path planning for moving target search. [Internet] [Doctoral dissertation]. University of Southern California; 2013. [cited 2019 Nov 21]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/236875/rec/3456.

Council of Science Editors:

Sun X. Incremental search-based path planning for moving target search. [Doctoral Dissertation]. University of Southern California; 2013. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/236875/rec/3456


University of Southern California

2. Zhao, Jing. Provenance management for dynamic, distributed and dataflow environments.

Degree: PhD, Computer Science, 2012, University of Southern California

Provenance, the derivation history of data objects, records how, when, and by whom a piece of data was created and modified. Provenance allows users to understand the context of derived data, estimate its quality for use, locate data of interest, and determine datasets affected by erroneous processes. Thus it is playing an important role in scientific experiments and business processes for data quality control, audit trail, and ensuring regulatory compliance. ❧ While most of the previous works only study provenance in a closed and well-controlled environment (e.g., a workflow engine), challenges still exist for holistic provenance management in practical and open environments, where provenance can be distributed, dynamic and diverse. For example, in the Energy Informatics domain, provenance is often collected from large-scale workflows across disciplines and organizations and thus is usually stored in distributed repositories. However, there has been limited research on reconstruction of and query over distributed provenance information. Meanwhile, recurrent and stream processing workflows can generate fine-grained provenance with overwhelming size that can be larger than the original dataset. Provenance storage approaches for efficiently managing such metadata volumes do not have adequate focus in literature. And lastly, the fact that legacy tools without automatic provenance collection functionalities are still widely used leads to the requirement of manual provenance annotation operations, which causes provenance to be incomplete. ❧ In this thesis, by using Energy Informatics as an exemplar domain, we design and develop algorithms and systems for managing provenance in dynamic, distributed and dataflow environments, that are motivated by real world challenges. In particular, we make the following contributions: (1) template-based algorithms that can efficiently store provenance information for dynamic datasets, (2) algorithms for reconstructing and querying provenance graphs from distributed provenance repositories, (3) semantic-based approaches for predicting incomplete provenance. We evaluate our research contributions with use cases from the Energy Informatics domain, including both Smart Oilfield and Smart Grid. The evaluation results demonstrate that our work can achieve efficient and scalable provenance management. As future work, we also discuss key challenges and initial solutions for presenting provenance across different granularities based on its usage context information. Advisors/Committee Members: Prasanna, Viktor K. (Committee Chair), Nakano, Aiichiro (Committee Member), Raghavendra, Raghu (Committee Member), Simmhan, Yogesh (Committee Member).

Subjects/Keywords: dataflow; distributed environments; provenance; provenance management; energy informatics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zhao, J. (2012). Provenance management for dynamic, distributed and dataflow environments. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/103144/rec/5299

Chicago Manual of Style (16th Edition):

Zhao, Jing. “Provenance management for dynamic, distributed and dataflow environments.” 2012. Doctoral Dissertation, University of Southern California. Accessed November 21, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/103144/rec/5299.

MLA Handbook (7th Edition):

Zhao, Jing. “Provenance management for dynamic, distributed and dataflow environments.” 2012. Web. 21 Nov 2019.

Vancouver:

Zhao J. Provenance management for dynamic, distributed and dataflow environments. [Internet] [Doctoral dissertation]. University of Southern California; 2012. [cited 2019 Nov 21]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/103144/rec/5299.

Council of Science Editors:

Zhao J. Provenance management for dynamic, distributed and dataflow environments. [Doctoral Dissertation]. University of Southern California; 2012. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/103144/rec/5299

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