Celia Escamilla-Rivera (May 1st 2020). Bayesian Deep Learning for Dark Energy [Online First], IntechOpen, DOI: 10.5772/intechopen.91466. Available from: https://www.intechopen.com/online-first/bayesian-deep-learning-for-dark-energy

Jing Chen and Junying Jia (March 29th 2020). Automatic Recognition of Tea Diseases Based on Deep Learning [Online First], IntechOpen, DOI: 10.5772/intechopen.91953.

avier B. Cabrera, Manuel F. Veiga, Diego X. Morales and Ricardo Medina (November 27th 2019). Reducing Power Losses in Smart Grids with Cooperative Game Theory, Advanced Communication and Control Methods for Future Smartgrids, Taha Selim Ustun, IntechOpen, DOI: 10.5772/intechopen.88568.

New Zealand

Realistically modelling behaviour and interaction of heterogeneous road users (pedestrians and vehicles) in mixed-traffic zones (a.k.a. shared spaces) is challenging. The dynamic nature of the environment, heterogeneity of transport modes, and the absence of classical traffic rules make realistic microscopic traffic simulation hard problems. Existing multi-agent-based simulations of shared spaces largely use an expert-based approach, combining a symbolic (e.g. rule-based) modelling and reasoning paradigm (e.g. using BDI representations of beliefs and plans) with the hand-crafted encoding of the actual decision logic. More recently, deep learning (DL) models are largely used to derive and predict trajectories based on e.g. video data. In-depth studies comparing these two kinds of approaches are missing. In this work, we propose an expert-based model called GSFM that combines Social Force Model and Game theory and a DL model called LSTM-DBSCAN that manipulates Long Short-Term Memories and density-based clustering for multi-agent trajectory prediction. We create a common framework to run these two models in parallel to guarantee a fair comparison. Real-world mixed traffic data from shared spaces of different layout are used to calibrate/train and evaluate the models. The empirical results imply that both models can generate realistic predictions, but they differ in the way of handling collisions and mimicking heterogeneous behaviour. Via a thorough study, we draw the conclusion of their respective strengths and weaknesses. 


AAMAS 2020

Trajectory Modelling in Shared Spaces: Expert-Based vs. Deep Learning Approach?

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

Browse keynotes, discussions, panels and over 300 presentations.


AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

technical paper

A Cooperative Approach Based on Local Detection of Similarities and Discontinuities for Brain MR Images Segmentation

Adaptivity in distributed agent-based simulation

Computationally Grounded Quantitative Trust with Time

Optimization of Large-scale Agent-based Simulations through Automated Abstraction and Simplification

Towards Agent-Based Traffic Simulation Using Live Data from Sensors for Smart Cities

Teaching Multiple Learning Agents by Environment-Dynamics Tweaks

Norm violation identification in online communities : A study of Stack Overflow comments

A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions

Improving Confidence in the Estimation of Values and Norms

On the Integration of Agents and Digital Twins in Healthcare

Trajectory Modelling in Shared Spaces: Expert-Based vs. Deep Learning Approach?

Downloads

Next from AAMAS 2020

A Cooperative Approach Based on Local Detection of Similarities and Discontinuities for Brain MR Images Segmentation

Similar lecture

Irresolute Approval-based Budgeting