The BDD-Based Dynamic A* Algorithm for Real-Time Replanning

Xu, Yanyan; Yue, Weiya; Su, Kaile

doi:10.1007/978-3-642-02270-8_28

Cited by 6 publications

(3 citation statements)

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“…Text pre-processing is the first step, but current Chinese text processing techniques are not suitable for MHK tests as stated above. In order to provide a more effective method that can be used in this step and a more accurate result for subsequent steps, we propose a WFSA-based algorithm to perform dynamic segmenting, detection and correction for erroneous Chinese characters simultaneously by using beam search (Steinbiss, Tran and Ney 1994) and heuristic search (Xu, Yue and Su 2009). In this algorithm, we use an n-gram language model to construct a WFSA.…”

Section: Main Contributionsmentioning

confidence: 99%

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SCESS: a WFSA-based automated simplified chinese essay scoring system with incremental latent semantic analysis

Hao

Deng

et al. 2014

Nat. Lang. Eng.

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Writing in language tests is regarded as an important indicator for assessing language skills of test takers. As Chinese language tests become popular, scoring a large number of essays becomes a heavy and expensive task for the organizers of these tests. In the past several years, some efforts have been made to develop automated simplified Chinese essay scoring systems, reducing both costs and evaluation time. In this paper, we introduce a system called SCESS (automated Simplified Chinese Essay Scoring System) based on Weighted Finite State Automata (WFSA) and using Incremental Latent Semantic Analysis (ILSA) to deal with a large number of essays. First, SCESS uses ann-gram language model to construct a WFSA to perform text pre-processing. At this stage, the system integrates a Confusing-Character Table, a Part-Of-Speech Table, beam search and heuristic search to perform automated word segmentation and correction of essays. Experimental results show that this pre-processing procedure is effective, with a Recall Rate of 88.50%, a Detection Precision of 92.31% and a Correction Precision of 88.46%. After text pre-processing, SCESS uses ILSA to perform automated essay scoring. We have carried out experiments to compare the ILSA method with the traditional LSA method on the corpora of essays from the MHK test (the Chinese proficiency test for minorities). Experimental results indicate that ILSA has a significant advantage over LSA, in terms of both running time and memory usage. Furthermore, experimental results also show that SCESS is quite effective with a scoring performance of 89.50%.

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Section: Main Contributionsmentioning

confidence: 99%

“…1994) and heuristic search (Xu et al . 2009). In this algorithm, we use an n -gram language model to construct a WFSA.…”

Section: Introductionmentioning

confidence: 99%

SCESS: a WFSA-based automated simplified chinese essay scoring system with incremental latent semantic analysis

Hao

Deng

et al. 2014

Nat. Lang. Eng.

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“…실시간 재계획(Real-time Replanning) 기법은 지도가 갱신될 때 로봇의 행동 계획을 빠르게 재수정하고 적용할 수 있도록 한다. 즉, 환경의 변화 에 빠르게 적응할 수 있도록 한다 [16][17][18]. 그러나 이 기법 의 알고리즘은 매우 복잡하고 이해하기 어려울 뿐만 아니라 분석과 확장도 용이하지 않다.…”

Section: 로봇의 계획수립unclassified

Ontology-based User Intention Recognition for Proactive Planning of Intelligent Robot Behavior

Jeon¹,

Choi²

2011

Journal of Korean Institute of Intelligent Systems

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Due to the uncertainty of intention recognition for behaviors of users, the intention is differently recognized according to the situation for the same behavior by the same user, the accuracy of user intention recognition by minimizing the uncertainty is able to be improved. This paper suggests a novel ontology-based method to recognize user intentions, and able to minimize the uncertainties that are the obstacles against the precise recognition of user intention. This approach creates ontology for user intention, makes a hierarchy and relationship among user intentions by using RuleML as well as Dynamic Bayesian Network, and improves the accuracy of user intention recognition by using the defined RuleML as well as the gathered sensor data such as temperature, humidity, vision, and auditory. To evaluate the performance of robot proactive planning mechanism, we developed a simulator, carried out some experiments to measure the accuracy of user intention recognition for all possible situations, and analyzed and detailed described the results. The result of our experiments represented relatively high level the accuracy of user intention recognition. On the other hand, the result of experiments tells us the fact that the actions including the uncertainty get in the way the precise user intention recognition.

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Solving Difficult SAT Problems by Using OBDDs and Greedy Clique Decomposition

Chen²,

et al. 2012

Frontiers in Algorithmics and Algorithmic Aspects in Information and Management

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The BDD-Based Dynamic A* Algorithm for Real-Time Replanning

Cited by 6 publications

References 6 publications

SCESS: a WFSA-based automated simplified chinese essay scoring system with incremental latent semantic analysis

SCESS: a WFSA-based automated simplified chinese essay scoring system with incremental latent semantic analysis

Ontology-based User Intention Recognition for Proactive Planning of Intelligent Robot Behavior

Solving Difficult SAT Problems by Using OBDDs and Greedy Clique Decomposition

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