Multi-Agent-Based Simulation XXI

Distributed agent-based simulations often suffer from an imbalance in computational load, leading to a suboptimal use of resources. This happens when part of the computational resoures are waiting idle for another process to finish. Self-adaptive load-balancing algorithms have been developed to use these resources more optimally. These algorithms are typically implemented ad-hoc, making re-usability and maintenance difficult. In this work, we present a generic self-adaptive framework. This methodology is evaluated with the Acsim framework on two simulations: a micro-traffic simulation and a cellular automata simulation. For each of these scenarios a scalable and adaptive load-balancing algorithm is implemented, showing significant improvements in execution time of the simulation.

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.

The Smart City and Internet-of-Things revolutions enable the collection of various types of data in real-time through sensors. This data can be used to improve the decision tools and simulations used by city planners. This paper presents a new framework for real-time traffic simulation integrating an agent-based methodology with live CCTV and other sensor data while respecting the privacy regulations. The framework simulates traffic flows of pedestrians, vehicles and bicycles and their interactions. The approach has been applied in Liverpool (NSW, Australia) showing promising preliminary results and can easily ingest additional sensor data, e.g. air quality.

Electric power sharing among households based on the bidding method is studied as a future service. In order to verify the feasibility of such a service, a new multi-agent simulation model has been designed. We validated this model through some evaluations. For example, it is confirmed that the market price on this service stably changes according to the supply-demand balance between both sold and purchased bid volumes. In addition to that, the results of the household profit and contract rate of this service showed that the design for bid strategies works as intended in most cases.

Controlling recurrent infectious diseases is a vital yet complicated problem. A large portion of the controlling epidemic relies on patients visit clinics voluntarily. However, they may already transmit the disease to their contacts by the time they feel sick enough to visit the clinic, especially for conditions with a long incubation period. Therefore, active screening/case finding was deployed to provide a powerful yet expensive means to control disease spread in recent years. To make active screening success a given limit budget, one of the challenges that need to be addressed is that we do not know the exact state of each patient. Given the number of horizon and budget we have in each time step, we also need to plan our screening efficiently and screening the vital patients in time. Thus, we apply a reinforcement learning approach to solve active screening problems on the network SIS disease model. The first contribution of this work is that we identify three significant challenges in active screening problems: partially observable states, combinatorial action choice, high-dimensional state-action space. We further propose the corresponding solutions to overcome these challenges. Specifically, we resolve the issue of high-dimensional state-action space by encoding the actions and partially observable states into a lower dimension form, which is done by either manually, using domain expertise, or automatically, using the state of the art GCN approach. We show that our approach can scale up to large graphs and perform decently compared to other baselines of previous literature and current practice.

This paper investigates the impact of changes in agents’ beliefs coupled with dynamics in agents’ meta-roles on the evolution of institutions. The study embeds agents’ meta-roles in the BDI architecture. In this context, the study scrutinises the impact of cognitive dissonance in agents due to unfairness of institutions. To showcase our model, two historical long-distance trading societies, namely Armenian merchants of New-Julfa and the English East India Company are simulated. Results show how change in roles of agents coupled with specific institutional characteristics leads to changes of the rules in the system.

Agent-based simulations of social media platforms often need to be run for many repetitions at large scale. Often, researchers must compromise between available computational resources (memory, run-time), the scale of the simulation, and the quality of its predictions.

As a step to support this process, we present a systematic exploration of simplifications of agent simulations across a number of dimensions suitable for social media studies. Simplifications explored include sub-sampling, implementing agents representing teams or groups of users, simplifying agent behavior, and simplifying the environment.

We also propose a tool that helps apply simplifications to a simulation model, and helps find simplifications that approximate the behavior of the full-scale simulation within computational resource limits.

We present experiments in two social media domains, GitHub and Twitter, using data both to design agents and to test simulation predictions against ground truth. Sub-sampling agents often provides a simple and effective strategy in these domains, particularly in combination with simplifying agent behavior, yielding up to an order of magnitude improvement in run-time with little or no loss in predictive power. Moreover, some simplifications improve performance over the full-scale simulation by removing noise.

We describe domain characteristics that may indicate the most effective simplification strategies and discuss heuristics for automatic exploration of simplifications.

We compare synthetic population-based travel demand modeling with the state of the art travel demand models used by metropolitan planning offices in the United States. Our comparison of the models for three US cities shows that synthetic population-based models match the state of the art models closely for the temporal trip distributions and the spatial distribution of destinations. The advantages of the synthetic population-based method are that it provides greater spatial resolution, can be generalized to any region, and can be used for studying correlations with demographics and activity types, which are useful for modeling the effects of policy changes.