51,711 research outputs found
Probabilistic Methodology and Techniques for Artefact Conception and Development
The purpose of this paper is to make a state of the art on probabilistic methodology and techniques for artefact conception and development. It is the 8th deliverable of the BIBA (Bayesian Inspired Brain and Artefacts) project. We first present the incompletness problem as the central difficulty that both living creatures and artefacts have to face: how can they perceive, infer, decide and act efficiently with incomplete and uncertain knowledge?. We then introduce a generic probabilistic formalism called Bayesian Programming. This formalism is then used to review the main probabilistic methodology
and techniques. This review is organized in 3 parts: first the probabilistic models from Bayesian networks to Kalman filters and from sensor fusion to CAD systems, second the inference techniques and finally the learning and model acquisition and comparison methodologies. We conclude with the perspectives of the BIBA project as they rise from this state of the art
A tutorial on recursive models for analyzing and predicting path choice behavior
The problem at the heart of this tutorial consists in modeling the path
choice behavior of network users. This problem has been extensively studied in
transportation science, where it is known as the route choice problem. In this
literature, individuals' choice of paths are typically predicted using discrete
choice models. This article is a tutorial on a specific category of discrete
choice models called recursive, and it makes three main contributions: First,
for the purpose of assisting future research on route choice, we provide a
comprehensive background on the problem, linking it to different fields
including inverse optimization and inverse reinforcement learning. Second, we
formally introduce the problem and the recursive modeling idea along with an
overview of existing models, their properties and applications. Third, we
extensively analyze illustrative examples from different angles so that a
novice reader can gain intuition on the problem and the advantages provided by
recursive models in comparison to path-based ones
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
Pedestrian Prediction by Planning using Deep Neural Networks
Accurate traffic participant prediction is the prerequisite for collision
avoidance of autonomous vehicles. In this work, we predict pedestrians by
emulating their own motion planning. From online observations, we infer a
mixture density function for possible destinations. We use this result as the
goal states of a planning stage that performs motion prediction based on common
behavior patterns. The entire system is modeled as one monolithic neural
network and trained via inverse reinforcement learning. Experimental validation
on real world data shows the system's ability to predict both, destinations and
trajectories accurately
Towards Intelligent Databases
This article is a presentation of the objectives and techniques
of deductive databases. The deductive approach to databases aims at extending
with intensional definitions other database paradigms that describe
applications extensionaUy. We first show how constructive specifications can
be expressed with deduction rules, and how normative conditions can be defined
using integrity constraints. We outline the principles of bottom-up and
top-down query answering procedures and present the techniques used for
integrity checking. We then argue that it is often desirable to manage with
a database system not only database applications, but also specifications of
system components. We present such meta-level specifications and discuss
their advantages over conventional approaches
MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs
We present multi-agent A* (MAA*), the first complete and optimal heuristic
search algorithm for solving decentralized partially-observable Markov decision
problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for
computing optimal plans for a cooperative group of agents that operate in a
stochastic environment such as multirobot coordination, network traffic
control, `or distributed resource allocation. Solving such problems efiectively
is a major challenge in the area of planning under uncertainty. Our solution is
based on a synthesis of classical heuristic search and decentralized control
theory. Experimental results show that MAA* has significant advantages. We
introduce an anytime variant of MAA* and conclude with a discussion of
promising extensions such as an approach to solving infinite horizon problems.Comment: Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005
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