Fuzzy Naive Bayesian Classification in RoboSoccer 3D: A Hybrid Approach to Decision Making

Bustamante, Carlos D.; Garrido, Leonardo; Soto, Rogelio

doi:10.1007/978-3-540-74024-7_52

Cited by 5 publications

(6 citation statements)

References 5 publications

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“…74 In practical situations, when a set of observations are made, evidence, such as speech, object, context, and action involved in cooperation, is usually not mutually independent. 82 As for task plan representation, this simplification brings both negative effects, such as undermining the plan representation accuracy, and positive effects, such as preventing overfitting problems in plan-representation process. The common problem of multi-joint-probabilistic BN models is that temporal associations are ignored, limiting the implementations of real-time NLC.…”

Section: Nl-based Execution Plan Generationmentioning

confidence: 99%

A review of methodologies for natural-language-facilitated human–robot cooperation

Liu

Zhang

2019

International Journal of Advanced Robotic Systems

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Natural-language-facilitated human-robot cooperation (NLC) refers to using natural language (NL) to facilitate interactive information sharing and task executions with a common goal constraint between robots and humans. Recently, NLC research has received increasing attention. Typical NLC scenarios include robotic daily assistance, robotic health caregiving, intelligent manufacturing, autonomous navigation, and robot social accompany. However, a thorough review, that can reveal latest methodologies to use NL to facilitate human-robot cooperation, is missing. In this review, a comprehensive summary about methodologies for NLC is presented. NLC research includes three main research focuses: NL instruction understanding, NLbased execution plan generation, and knowledgeworld mapping. In-depth analyses on theoretical methods, applications, and model advantages and disadvantages are made. Based on our paper review and perspective, potential research directions of NLC are summarized. Index Terms-natural language, human-robot cooperation, NL instruction understanding, NLbased execution plan generation, knowledge-world mapping _______________________________________ Rui Liu and Xiaoli Zhang are with the Intelligent Robotics and Systems Lab (IRSL), Colorado School of Mines,

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Section: Nl-based Execution Plan Generationmentioning

confidence: 99%

A review of methodologies for natural-language-facilitated human–robot cooperation

Liu

Zhang

2019

International Journal of Advanced Robotic Systems

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“…However, empirical comparisons of Bayesian classifiers, decision trees, and neural networks have found them to be comparable in some application domains. [59][60][61] Inductive learning is perhaps the most commonly used approaches for extracting knowledge from a collection of data. Inductive learning techniques are fast compared to other techniques.…”

Section: Existing Algorithms For Learning Fuzzy Classification Rulesmentioning

confidence: 99%

A novel algorithm for fuzzy rule induction in data mining

Afify

2013

Proceedings of the Institution of Mechanical Engineers, Part C:

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Rule induction as a method of constructing classifiers is of particular interest to data mining because it generates models in the form of If-Then rules which are more expressive and easier for humans to comprehend and check. Several induction algorithms have been developed to learn classification rules. However, most of these algorithms are based on ‘crisp’ data and produce ‘crisp’ models. This paper presents FuzzySRI, a novel algorithm based on the techniques of fuzzy sets and fuzzy logic for inducing fuzzy classification rules. The algorithm possesses the clear knowledge representation capability of rule induction methods and the ability of fuzzy techniques to handle vague information. Experimental results show that FuzzySRI can outperform other fuzzy and non-fuzzy learning systems in terms of predictive accuracy, comprehensibility, and computational efficiency. It is also shown that FuzzySRI can be successfully applied to an industrial application concerning the automatic identification of machine faults.

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“…Furthermore, it is compared versus a Gaussian Naive Bayes classifier, another approach of handling continuous attributes. Initial results obtained on this chapter for the Fuzzy Naive Bayesian classifier applied to decision making in RoboCup 3D were published in (Bustamante et al, 2006). Later performance comparison to Gaussian Naive Bayes classifier in the same pass skill scenario was published in (Bustamante et al, 2006b).…”

Section: Probabilistic Decision Makingmentioning

confidence: 99%

Probabilistic and Statistical Layered Approach for High-Level Decision Making in Soccer Simulation Robotics

Bustamante¹,

Garrido²

2007

Robotic Soccer

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Fuzzy Naive Bayesian Classification in RoboSoccer 3D: A Hybrid Approach to Decision Making

Cited by 5 publications

References 5 publications

A review of methodologies for natural-language-facilitated human–robot cooperation

A review of methodologies for natural-language-facilitated human–robot cooperation

A novel algorithm for fuzzy rule induction in data mining

Probabilistic and Statistical Layered Approach for High-Level Decision Making in Soccer Simulation Robotics

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