What is a Good Pattern of Life Model? Guidance for Simulations

We have been modeling an ever-increasing scale of applications with agents that simulate the pattern of life (PoL) and real-world human behaviors in diverse regions of the world. The goal is to support sociocultural training and analysis. To measure progress, we propose the definition of a measure of goodness for such simulated agents, and review the issues and challenges associated with first-generation (1G) agents. Then we present a second generation (2G) agent hybrid approach that seeks to improve realism in terms of emergent daily activities, social awareness, and micro-decision making in simulations. We offer a PoL case study with a mix of 1G and 2G approaches that was able to replace the pucksters and avatar operators needed in large-scale immersion exercises. We conclude by observing that a 1G PoL simulation might still be best where large-scale, pre-scripted training scenarios will suffice, while the 2G approach will be important for analysis or if it is vital to learn about adaptive opponents or unexpected or emergent effects of actions. Lessons are shared about ways to blend 1G and 2G approaches to get the best of each

Developing and Researching PhET simulations for Teaching Quantum Mechanics

Quantum mechanics is difficult to learn because it is counterintuitive, hard to visualize, mathematically challenging, and abstract. The Physics Education Technology (PhET) Project, known for its interactive computer simulations for teaching and learning physics, now includes 18 simulations on quantum mechanics designed to improve learning of this difficult subject. Our simulations include several key features to help students build mental models and intuitions about quantum mechanics: visual representations of abstract concepts and microscopic processes that cannot be directly observed, interactive environments that directly couple students' actions to animations, connections to everyday life, and efficient calculations so students can focus on the concepts rather than the math. Like all PhET simulations, these are developed using the results of education research and feedback from educators, and are tested in student interviews and classroom studies. This article provides an overview of the PhET quantum simulations and their development. We also describe research demonstrating their effectiveness and share some insights about student thinking that we have gained from our research on quantum simulations.Comment: accepted by American Journal of Physics; v2 includes an additional study, more explanation of research behind claims, clearer wording, and more reference

A Comparison of System Optimal and User Optimal Route Guidance.

The work described in this paper (carried out under the EC `DRIVE' programme) extends the simulations described in Working Paper 315, with the aim of studying the likely benefits to and reactions of drivers to system optimal (SO) route guidance - in particular, these effects are compared with those obtained under user optimal (UE) guidance. The model used is again one of a multiple user class equilibrium assignment, so that equipped drivers may be directed to more than one route per origin-destination movement. UE and SO guidance are compared, at different levels of equipped vehicles and demand levels, on the basis of the number of routes they recommend and the similarity of the flows on these routes, as well as link-based properties such as actual flows and queues resulting. These serve to demonstrate the extent to which the routes recommended under UE guidance serve as proxies to those under SO guidance. Secondly, a comparison is made of average (dis)benefits to guided drivers as well as the excess travel time incurred by individual equipped drivers in following SO, as opposed to UE guidance, in order to determine the extent of user sub-optimality of SO routing. Thirdly, input from a parallel DRIVE project, investigating user reactions to guidance information, is used to infer the extent to which drivers are likely to accept the sub-optimality of SO guidance, and the factors which are likely to influence their acceptance. Finally, some preliminary analysis is performed on combined strategies, which aim to strike a balance between the system benefits of SO guidance and the user benefits of UE routing

A Comparison of System Optimal and User Optimal Route Guidance.

The work described in this paper (carried out under the EC `DRIVE' programme) extends the simulations described in Working Paper 315, with the aim of studying the likely benefits to and reactions of drivers to system optimal (SO) route guidance - in particular, these effects are compared with those obtained under user optimal (UE) guidance. The model used is again one of a multiple user class equilibrium assignment, so that equipped drivers may be directed to more than one route per origin-destination movement. UE and SO guidance are compared, at different levels of equipped vehicles and demand levels, on the basis of the number of routes they recommend and the similarity of the flows on these routes, as well as link-based properties such as actual flows and queues resulting. These serve to demonstrate the extent to which the routes recommended under UE guidance serve as proxies to those under SO guidance. Secondly, a comparison is made of average (dis)benefits to guided drivers as well as the excess travel time incurred by individual equipped drivers in following SO, as opposed to UE guidance, in order to determine the extent of user sub-optimality of SO routing. Thirdly, input from a parallel DRIVE project, investigating user reactions to guidance information, is used to infer the extent to which drivers are likely to accept the sub-optimality of SO guidance, and the factors which are likely to influence their acceptance. Finally, some preliminary analysis is performed on combined strategies, which aim to strike a balance between the system benefits of SO guidance and the user benefits of UE routing

System upgrade: realising the vision for UK education

A report summarising the findings of the TEL programme in the wider context of technology-enhanced learning and offering recommendations for future strategy in the area was launched on 13th June at the House of Lords to a group of policymakers, technologists and practitioners chaired by Lord Knight. The report – a major outcome of the programme – is written by TEL director Professor Richard Noss and a team of experts in various fields of technology-enhanced learning. The report features the programme’s 12 recommendations for using technology-enhanced learning to upgrade UK education

Decentralized UAV guidance using modified boid algorithms

Decentralized guidance of Unoccupied Air Vehicles (UAVs) is a very challenging problem. Such technology can lead to improved safety, reduced cost, and improved mission efficiency. Only a few ideas for achieving decentralized guidance exist, the most effective being the boid algorithm. Boid algorithms are rule-based guidance methods derived from observations of animal swarms. In this paper, boid rules are used to autonomously control a group of UAVs in high-level transit simulations. This paper differs from previous work in that, as an alternative to using exponentially scaled behavior weightings, the weightings are computed off-line and scheduled according to a contingency management system. The motivation for this technique is to reduce the amount of on-line computation required by the flight system. Many modifications to the basic boid algorithm are required in order to achieve a flightworthy design. These modifications include the ability to define flight areas, limit turning maneuvers in accordance with the aircraft dynamics, and produce intelligent waypoint paths. The use of a contingency management system is also a major modification to the boid algorithm. A Simple Genetic Algorithm is used to partially optimize the behavior weightings of the boid algorithm. While a full optimization of all contingencies is not performed due to computation requirements, the framework for such a process is developed. Wolfram\u27s Matlab software is used to develop and simulate the boid guidance algorithm. The algorithm is interfaced with Cloud Cap Technology\u27s Piccolo autopilot system for Hardware-in-the-Loop simulations. These high-fidelity simulations prove this technology is both feasible and practical. They also prove the boid guidance system developed herein is suitable for comprehensive flight testing