Activity, context, and plan recognition with computational causal behavior models

Frank Krüger

doi:10.18453/rosdok_id00002015

Frank Krüger

Activity, context, and plan recognition with computational causal behavior models

Universität Rostock, 2018

https://doi.org/10.18453/rosdok_id00002015

Abstract: Objective of this thesis is to answer the question "how to achieve efficient sensor-based reconstruction of causal structures of human behaviour in order to provide assistance?". To answer this question, the concept of Computational Causal Behaviour Models (CCBMs) is introduced. CCBM allows the specification of human behaviour by means of preconditions and effects and employs Bayesian filtering techniques to reconstruct action sequences from noisy and ambiguous sensor data. Furthermore, a novel approximative inference algorithm – the Marginal Filter – is introduced.

Dissertation Freier Zugang

Titel:

Activity, context, and plan recognition with computational causal behavior models

Beteiligte Personen:

Frank Krüger[VerfasserIn]
	1079875972
Thomas Kirste , Dr.-Ing.[AkademischeR BetreuerIn]
	137244754
	Universität Rostock, Fakultät für Informatik
Adelinde Uhrmacher , Dr. rer. nat.[AkademischeR BetreuerIn]
	136258794
	Universität Rostock, Fakultät für Informatik
Frank Ortmeier , Dr.-Ing.[AkademischeR BetreuerIn]
	131862758
	Otto-von-Guericke-Universität Magdeburg, Fakultät für Informatik

Beteiligte Körperschaften:

Universität Rostock, Fakultät für Informatik und Elektrotechnik[Grad-verleihende Institution]
	10085032-7

Zusammenfassung:

Objective of this thesis is to answer the question "how to achieve efficient sensor-based reconstruction of causal structures of human behaviour in order to provide assistance?". To answer this question, the concept of Computational Causal Behaviour Models (CCBMs) is introduced. CCBM allows the specification of human behaviour by means of preconditions and effects and employs Bayesian filtering techniques to reconstruct action sequences from noisy and ambiguous sensor data. Furthermore, a novel approximative inference algorithm – the Marginal Filter – is introduced. [Englisch]

Dokumenttyp:

Dissertation

Einrichtung:

Fakultät für Informatik und Elektrotechnik

Sprache:

Englisch

Sachgruppe der DNB:

004 Informatik

Veröffentlichung /
Entstehung:

Rostock

Rostock: Universität Rostock

2018