A Logical Hierarchical Hidden Semi-Markov Model for Team Intention Recognition

Yue, Shiguang; Jiao, Peng; Zha, Yabing; Yin, Qinye

doi:10.1155/2015/975951

Cited by 3 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overfitting and underfitting problems also exist in the traditional GM during the training phase, similar to other approximation models. As shown in (14), the developed coefficient̂and Grey-controlled variablêin the GM are derived from the least squares method.…”

Section: Modeling Methods Of a Novel Gm Based Onmentioning

confidence: 99%

“…Approximate mathematical models based on datadriven approaches are acceptable substitutes for accurate physical models that are unlikely obtained in fields of multidisciplinary design optimization, prediction, and so on. Many approximation models with random and nonlinear features have been constructed, and they include the response surface model (RS) [3,4], polynomial regression model [5,6], autoregressive moving average (ARMA) [7,8], artificial neural network model (ANN) [9,10], support vector machines (SVM) [11,12], hidden Markov model (HMM) [13,14], and Grey model (GM) [15,16]. These approximation models are widely used in nonlinear simulation, classification, regression, and other domains.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Study on the Generalized Approximation Modeling Method Based on Fitting Sensitivity for Prediction of Engine Performance

Lin

Wang

Zhong

2017

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

Prediction technology for aeroengine performance is significantly important in operational maintenance and safety engineering. In the prediction of engine performance, to address overfitting and underfitting problems with the approximation modeling technique, we derived a generalized approximation model that could be used to adjust fitting precision. Approximation precision was combined with fitting sensitivity to allow the model to obtain excellent fitting accuracy and generalization performance. Taking the Grey model (GM) as an example, we discussed the modeling approach of the novel GM based on fitting sensitivity, analyzed the setting methods and optimization range of model parameters, and solved the model by using a genetic algorithm. By investigating the effect of every model parameter on the prediction precision in experiments, we summarized the change regularities of the root-mean-square errors (RMSEs) varying with the model parameters in novel GM. Also, by analyzing the novel ANN and ANN with Bayesian regularization, it is concluded that the generalized approximation model based on fitting sensitivity can achieve a reasonable fitting degree and generalization ability.

show abstract

Section: Modeling Methods Of a Novel Gm Based Onmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Study on the Generalized Approximation Modeling Method Based on Fitting Sensitivity for Prediction of Engine Performance

Lin

Wang

Zhong

2017

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

show abstract

“…Logical Hidden Markov Models (LHMMs) (Kersting, De Raedt, & Raiko, 2006;Natarajan, Bui, Tadepalli, Kersting, & Wong, 2008;Yue, Xu, Qin, & Yin, 2015b;Yue, Jiao, Zha, & Yin, 2015a) are similar to Hidden Markov Models (HMMs), except that each state consists of a logical atom. A LHMM transition consists of two steps.…”

Section: B5 Logical Hidden Markov Modelsmentioning

confidence: 99%

State-Space Abstractions for Probabilistic Inference: A Systematic Review

Lüdtke¹,

Schröder²,

Bader³

et al. 2018

jair

View full text Add to dashboard Cite

Tasks such as social network analysis, human behavior recognition, or modeling biochemical reactions, can be solved elegantly by using the probabilistic inference framework. However, standard probabilistic inference algorithms work at a propositional level, and thus cannot capture the symmetries and redundancies that are present in these tasks.Algorithms that exploit those symmetries have been devised in different research fields, for example by the lifted inference-, multiple object tracking-, and modeling and simulationcommunities. The common idea, that we call state space abstraction, is to perform inference over compact representations of sets of symmetric states. Although they are concerned with a similar topic, the relationship between these approaches has not been investigated systematically.This survey provides the following contributions. We perform a systematic literature review to outline the state of the art in probabilistic inference methods exploiting symmetries. From an initial set of more than 4,000 papers, we identify 116 relevant papers. Furthermore, we provide new high-level categories that classify the approaches, based on common properties of the approaches. The research areas underlying each of the categories are introduced concisely. Researchers from different fields that are confronted with a state space explosion problem in a probabilistic system can use this classification to identify possible solutions. Finally, based on this conceptualization, we identify potentials for future research, as some relevant application domains are not addressed by current approaches.

show abstract

“…In the second and the third parts, performances were evaluated by statistic metrics: precision, recall, andmeasure. Their meanings and computation details can be found in [31]. The value of the three metrics is between 0 and 1; a higher value means a better performance.…”

Section: Settingsmentioning

confidence: 99%

A Decentralized Partially Observable Decision Model for Recognizing the Multiagent Goal in Simulation Systems

Yue

Yordanova

Kirste

et al. 2016

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

Multiagent goal recognition is important in many simulation systems. Many of the existing modeling methods need detailed domain knowledge of agents’ cooperative behaviors and a training dataset to estimate policies. To solve these problems, we propose a novel decentralized partially observable decision model (Dec-POMDM), which models cooperative behaviors by joint policies. In this compact way, we only focus on the distribution of joint policies. Additionally, a model-free algorithm, cooperative colearning based on Sarsa, is exploited to estimate agents’ policies under the assumption of rationality, which makes the training dataset unnecessary. In the inference, considering that the Dec-POMDM is discrete and its state space is large, we implement a marginal filter (MF) under the framework of the Dec-POMDM, where the initial world states and results of actions are uncertain. In the experiments, a new scenario is designed based on the standard predator-prey problem: we increase the number of preys, and our aim is to recognize the real target of predators. Experiment results show that (a) our method recognizes goals well even when they change dynamically; (b) the Dec-POMDM outperforms supervised trained HMMs in terms of precision, recall, and F-measure; and (c) the MF infers goals more efficiently than the particle filter under the framework of the Dec-POMDM.

show abstract

A Logical Hierarchical Hidden Semi-Markov Model for Team Intention Recognition

Cited by 3 publications

References 24 publications

A Study on the Generalized Approximation Modeling Method Based on Fitting Sensitivity for Prediction of Engine Performance

A Study on the Generalized Approximation Modeling Method Based on Fitting Sensitivity for Prediction of Engine Performance

State-Space Abstractions for Probabilistic Inference: A Systematic Review

A Decentralized Partially Observable Decision Model for Recognizing the Multiagent Goal in Simulation Systems

Contact Info

Product

Resources

About