Optimizing mobile electronic program guide by adaptive learning

Chengyuan Peng; Petri Vuorimaa

Optimizing mobile electronic program guide by adaptive learning

^*Corresponding author for this work

Not published at VTT

Helsinki University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

Abstract

The Electronic Program Guide (EPG) is the most important service of future mobile digital television platform. In this paper, we present an approach which applying adaptive learning algorithms to the EPG agent to automatically learn optimized channel categorization and TV event list prompts. We apply a learning Classifier System (especially XCS) as a reinforcement learning component to learn policies (a set of rules). We embed usability heuristics of mobile digital television in the reward functions, which are used to achieve the agent's goals. We test this approach and demonstrate that the accuracy of channel classification can be as high as 99% in average.

Original language	English
Title of host publication	Proceedings of the Second IASTED International Conference on Communications, Internet, and Information Technology
Editors	M.H. Hamza, M.H. Hamza
Publisher	Acta Press
Pages	814-819
Number of pages	6
ISBN (Print)	0-889-86398-9
Publication status	Published - 1 Dec 2003
MoE publication type	A4 Article in a conference publication
Event	2nd IASTED International Conference on Communications, Internet, and Information Technology - Scottdale, AZ, United States Duration: 17 Nov 2003 → 19 Nov 2003

Conference

Conference	2nd IASTED International Conference on Communications, Internet, and Information Technology
Country/Territory	United States
City	Scottdale, AZ
Period	17/11/03 → 19/11/03

Keywords

Channel Categorization
Mobile Digital Television
Reinforcement Learning
Reward Function
XCS

Cite this

@inproceedings{a9b538b5e6864c509f575d785ae3d13c,

title = "Optimizing mobile electronic program guide by adaptive learning",

abstract = "The Electronic Program Guide (EPG) is the most important service of future mobile digital television platform. In this paper, we present an approach which applying adaptive learning algorithms to the EPG agent to automatically learn optimized channel categorization and TV event list prompts. We apply a learning Classifier System (especially XCS) as a reinforcement learning component to learn policies (a set of rules). We embed usability heuristics of mobile digital television in the reward functions, which are used to achieve the agent's goals. We test this approach and demonstrate that the accuracy of channel classification can be as high as 99% in average.",

keywords = "Channel Categorization, Mobile Digital Television, Reinforcement Learning, Reward Function, XCS",

author = "Chengyuan Peng and Petri Vuorimaa",

year = "2003",

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booktitle = "Proceedings of the Second IASTED International Conference on Communications, Internet, and Information Technology",

publisher = "Acta Press",

address = "Canada",

note = "2nd IASTED International Conference on Communications, Internet, and Information Technology ; Conference date: 17-11-2003 Through 19-11-2003",

}

Peng, C & Vuorimaa, P 2003, Optimizing mobile electronic program guide by adaptive learning. in MH Hamza & MH Hamza (eds), Proceedings of the Second IASTED International Conference on Communications, Internet, and Information Technology., 403-012, Acta Press, pp. 814-819, 2nd IASTED International Conference on Communications, Internet, and Information Technology, Scottdale, AZ, United States, 17/11/03.

Optimizing mobile electronic program guide by adaptive learning. / Peng, Chengyuan; Vuorimaa, Petri.
Proceedings of the Second IASTED International Conference on Communications, Internet, and Information Technology. ed. / M.H. Hamza; M.H. Hamza. Acta Press, 2003. p. 814-819 403-012.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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AU - Vuorimaa, Petri

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N2 - The Electronic Program Guide (EPG) is the most important service of future mobile digital television platform. In this paper, we present an approach which applying adaptive learning algorithms to the EPG agent to automatically learn optimized channel categorization and TV event list prompts. We apply a learning Classifier System (especially XCS) as a reinforcement learning component to learn policies (a set of rules). We embed usability heuristics of mobile digital television in the reward functions, which are used to achieve the agent's goals. We test this approach and demonstrate that the accuracy of channel classification can be as high as 99% in average.

AB - The Electronic Program Guide (EPG) is the most important service of future mobile digital television platform. In this paper, we present an approach which applying adaptive learning algorithms to the EPG agent to automatically learn optimized channel categorization and TV event list prompts. We apply a learning Classifier System (especially XCS) as a reinforcement learning component to learn policies (a set of rules). We embed usability heuristics of mobile digital television in the reward functions, which are used to achieve the agent's goals. We test this approach and demonstrate that the accuracy of channel classification can be as high as 99% in average.

KW - Channel Categorization

KW - Mobile Digital Television

KW - Reinforcement Learning

KW - Reward Function

KW - XCS

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M3 - Conference article in proceedings

AN - SCOPUS:1542643700

SN - 0-889-86398-9

SP - 814

EP - 819

BT - Proceedings of the Second IASTED International Conference on Communications, Internet, and Information Technology

A2 - Hamza, M.H.

PB - Acta Press

T2 - 2nd IASTED International Conference on Communications, Internet, and Information Technology

Y2 - 17 November 2003 through 19 November 2003

ER -

Optimizing mobile electronic program guide by adaptive learning

Abstract

Conference

Keywords

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