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

Survey: Probabilistic Methodology and Techniques for Artefact Conception and Development

Projet CYBERMOVEThe 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

Modeling the influence of structural lifecycle events on activity-travel decisions using a structure learning algorithm

This paper describes the results of a study on the impact of lifecycle events on activity-travel choice decisions of individuals. An Internet-based survey was designed to collect data concerning structural lifecycle events. In addition, respondents answered questions about personal and household characteristics, possession and availability of transport modes and their current travel behavior. In total, 710 respondents completed the online survey. The complexity of transport mode choice is modeled using a Bayesian Belief Network. Previous papers describe the conceptual framework underlying the model and the temporal effects of lifecycle events on mode choice. This paper focuses on influences of structural life trajectory events on each other and on changes in resources that impact activity-travel decisions. We investigate the extent to which causal relations exist between these events and their direct and indirect effects on changes in transport mode availability and the possession of transit passes. A structure learning algorithm is used to build a Bayesian Belief Network of interdependencies between these events from the data

Towards multimodal affective expression:merging facial expressions and body motion into emotion

Affect recognition plays an important role in human everyday life and it is a substantial way of communication through expressions. Humans can rely on different channels of information to understand the affective messages communicated with others. Similarly, it is expected that an automatic affect recognition system should be able to analyse different types of emotion expressions. In this respect, an important issue to be addressed is the fusion of different channels of expression, taking into account the relationship and correlation across different modalities. In this work, affective facial and bodily motion expressions are addressed as channels for the communication of affect, designed as an emotion recognition system. A probabilistic approach is used to combine features from two modalities by incorporating geometric facial expression features and body motion skeleton-based features. Preliminary results show that the presented approach has potential for automatic emotion recognition and it can be used for human robot interaction

Developing Reactive Distributed Aerial Robotics Platforms for Real-time Contaminant Mapping

The focus of this research is to design a sensor data aggregation system and centralized sensor-driven trajectory planning algorithm for fixed-wing aircraft to optimally assist atmospheric simulators in mapping the local environment in real-time. The proposed application of this work is to be used in the event of a hazardous contaminant leak into the atmosphere as a fleet of sensing unmanned aerial vehicles (UAVs) could provide valuable information for evacuation measures. The data aggregation system was designed using a state-of-the-art networking protocol and radio with DigiMesh and a process/data management system in the ROS2 DDS. This system was tested to consistently operate within the latencies and distances tolerated for the project while being highly extensible to sensor configurations. The problem of creating optimal trajectory planning for exploration has been modelled accurately using partially-observable Markov decision processes (POMDP). Deep Reinforcement learning (DRL) is commonly applied to approximate optimal solutions within a POMDP as it can be analytically intractable for complex state spaces. This research produces a POMDP that describes this exploration problem and applies the state-of-the-art soft actor-critic (SAC) reinforcement learning algorithm to create a policy that produces near-optimal trajectories within this new POMDP. A subset of the spatially relevant inputis used instead of complete state during training and a turn-taking sequential planner is designed for using multiple UAVs to help mitigate scalability problems that come with multi-UAV coordination. The learned policy from SAC can outperform a greedy and fixed trajectory on 1, 2, and 3 UAVs by a 30% margin on average. The turn-taking strategy provides small, but repeatable scaling benefits while the windowed input results in a 50%-60% increase in reward versus trained networks without windowed input. The proposed planning algorithm is effective in dynamic map exploration and has the potential to increase UAV effectiveness in atmospheric contaminant leak monitoring as it is expanded to be integrated on real-world UAVs