In supervised learning, many techniques focus on optimizing training phase to increase prediction performance. Active inference, a relatively novel paradigm, aims to decrease overall prediction error via selective collection of some labels based on relations among instances. In this research, we use dynamic Bayesian networks to model temporal systems and we apply active inference to dynamically...

Worldwide, conservation agencies employ rangers to protect conservation areas from poachers. However, agencies lack the manpower to have rangers effectively patrol these vast areas frequently. While past work has modeled poachers’ behavior so as to aid rangers in planning future patrols, those models’ predictions were not validated by extensive field tests. In this paper, we present a hybrid sp...

This paper presents a smart surveillance system named CASSANDRA, aimed at detecting instances of aggressive human behavior in public environments. A distinguishing aspect of CASSANDRA is the exploitation of complementary audio and video cues to disambiguate scene activity in real-life environments. From the video side, the system uses overlapping cameras to track persons in 3D and to extract fe...

This paper introduces online Bayesian network learning in detail. The structural and parametric learning abilities of the online Bayesian network learning are explored. The paper starts with revisiting the multi-agent self-organization problem and the proposed solution. Then, we explain the proposed Bayesian network learning, three scoring functions, namely LogLikelihood, Minimum description le...

With the continuous development of artificial intelligence technology and information technology, a large number background data are constantly generated. How to obtain effective useful in complex group becomes important meaningful. The traditional Bayesian network can represent probability distribution variables from based on graphical models. It has relatively clear reliable reasoning ability...

Learning from Observation (a.k.a. learning from demonstration) studies how computers can learn to perform complex tasks by observing and thereafter imitating the performance of an expert. Most work on learning from observation assumes that the behavior to be learned can be expressed as a state-to-action mapping. However most behaviors of interest in real applications of learning from observatio...