*FINDINGS fcws foth ngas mgas lgas dim cflt lcab ncrn scrn ocrn grnd temp egas *CLASSES wait open gas rflt clen gbat nstr cws *RULES if lgas then flod(0.80) if between(temp, 0, 50) AND nofm(1, scrn , ocrn) then chok(0.60) if egas then empt(0.90) if cflt then filt(1.00) if lcab then cab(0.90) if scrn AND dim then batd(0.70) if ncrn AND dim then batd(0.90) if ncrn AND not dim then strt(0.70) if g...

Learning consistent policies in decentralized settings is often problematic. The agents have a myopic view of their neighboring states that could lead to inconsistent action choices. The fundamental question addressed in this work is how to determine and obtain the minimal overlapping context among decentralized decision makers required to make their decisions more consistent. Our approach is a...

We review three recently proposed scan statistic methods for multivariate pattern detection. Each method models the relationship between multiple observed and hidden variables using a Bayesian network structure, drawing inferences about the underlying pattern type and the affected subset of the data. We first discuss the multivariate Bayesian scan statistic (MBSS) proposed by Neill and Cooper (...

To ensure privacy for route planning applications and other location based services (LBS), the service provider must be prevented from tracking a user’s path during navigation on the application level. However, the navigation functionality must be preserved. We introduce the algorithm PARTS to split route requests into route parts which will be submitted to an LBS in an unlinkable way. Equipped...

This paper presents an approach to predict future motion of a moving object based on its past movement. This approach is capable of learning object movement in an open environment, which is one of the limitions in some prior works. The proposed approach exploits the similarities of short-term movement behaviors by modeling a trajectory as concatenation of short segments. These short segments ar...

In this paper we examine the problem of inference in Bayesian Networks with discrete random variables that have very large or even unbounded domains. For example, in a domain where we are trying to identify a person, we may have variables that have as domains, the set of all names, the set of all postal codes, or the set of all credit card numbers. We cannot just have big tables of the conditio...

We present a cognitive model performing the Dynamic Stocks&Flows control task, in which subjects control a system by counteracting a systematically changing external variable. The model uses a metacognitive layer that chooses a task strategy drawn from of two classes of strategies: precise calculation and imprecise estimation. The model, formulated within the ACT-R theory, monitors the success ...

In this paper we propose an agent-based approach for controlling the behavior of a Smart Home Environment that, based on the recognized situation and user goal, selects a suitable workflow for combining services of the environment. To this aim we have developed a butler agent that employs user and context modeling for supporting proactive adaptation of the interaction with the house. The user c...