MASON is an open source multiagent simulation library geared towards simulating very large numbers of relatively lightweight interacting agents. On June 15 and 16, 2013, approximately two dozen invitees convened at George Mason University to discuss future directions for MASON and needs of the MASON community. This meeting formed the 2013 MASON NSF Workshop. During the Workshop, participants identified nine areas of interest to the community where they felt MASON should be improved or extended. This document details the reports of those working groups.

Students are often confused by what constitutes plagiarism in academia and the seriousness with which the offense is treated. The objective of this essay is to explain the issue and provide examples to explain what is plagiarism, and hopefully act as a useful guide.

Model-based testers design tests in terms of models, such as paths in graphs. Abstract tests cannot be run directly because they use names and events that exist in the model, but not the implementation. Testers usually solve this mapping problem by hand. Model elements often appear in many abstract tests, so testers write the same redundant code many times. This is time-consuming, labor-intensive, and error-prone. This paper presents a language to automate the creation of mappings from abstract tests to concrete tests. Three issues are addressed: (1) creating mappings and generating test values, (2) transforming graphs and using coverage criteria to generate test paths, and (3) solving constraints and generating concrete test. The paper also presents results from an empirical comparison of testers using the mapping language with manual mapping on 17 open source and example programs. We found that the automated test generation method took a fraction of the time the manual method took, and the manual tests contained numerous errors in which concrete tests did not match their abstract tests.

Advancements in robotic navigation, object search and exploration rest to a large extent on robust, efficient and more advanced semantic understanding of the surrounding environment. Since the choice of most relevant semantic information depends on the task, it is desirable to develop approaches which can be adopted for different tasks at hand and which separate the aspects related to surroundings from object entities. In the proposed work we present an efficient approach for detecting generic objects in urban environments from videos acquired by a moving vehicle by means of semantic segmentation. Compared to traditional approaches for semantic labeling, we strive to detect variety of objects, while avoiding the need for large amounts of training data required for recognizing individual object categories and visual variability within and across the categories. In the proposed approach we exploit the features providing evidence about widely available non-object categories (such as sky, road, buildings) and use informative features which are indicative of the presence of object boundaries to gather the evidence about objects. We formulate the object/non-object semantic segmentation problem in the Conditional Random Field Framework, where the structure of the graph is induced by the minimum spanning tree computed over 3D reconstruction, yielding an efficient algorithm for an exact inference. We carry out extensive experiments on videos of urban environments acquired by a moving vehicle and compare our approach to existing alternatives.

This paper presents a modular approach to testing concurrent programs that are modeled using labeled transition systems. Correctness is defined in terms of an implementation relation that is expected to hold between a model of the system and its implementation. The novelty of our approach is that the correctness of a concurrent software system is determined by testing the individual threads separately, without testing the system as a whole. We define a modular implementation relation on individual treads and show that modular relations can be tested separately in order to verify a (non-modular) implementation relation for the complete system. Empirical results indicate that our approach can significantly reduce the number of test sequences that are generated and executed during model-based testing.

Collision detection is a fundamental geometric tool for sampling-based motion planners. On the contrary, collision prediction for the scenarios that obstacle's motion is unknown is still in its infancy. This paper proposes a new approach to predict collision by assuming that obstacles are adversarial. Our new tool advances collision prediction beyond the translational and disc robots; arbitrary polygons with rotation can be used to better represent obstacles and provide tighter bound on predicted collision time. Comparing to an online motion planner that replans periodically at fixed time interval, our experimental results provide strong evidences that the new method significantly reduces the number of replans while maintaining higher success rate of finding a valid path.

In this paper, we investigate the issues that arise in the use of network capabilities to facilitate DoS prevention and mitigation in mobile ad hoc networks. Most proposals for capability-based protocols in wired networks depend upon routers on the path between the sender and receiver maintaining state that enables them to verify a capability. Frequent route changes make it necessary for any capability-based protocol for MANETs to re-establish this state as efficiently as possible. We propose EPIC, a method based on reverse-disclosure hash chains to support the establishment of path-independent capabilities and show how they can be efficiently maintained in a high mobility environment. Simulation results show that EPIC provides a 19.3% increase in performance and a 37.8% increase in efficiency over route-dependent capabilities.

Concurrent learning is a form of cooperative multiagent learning in which each agent has an independent learning process and little or no control over its teammates' actions. In such learning algorithms, an agent's perception of the joint search space depends on the reward received by both agents, which in turn depends on the actions currently chosen by the other agents. The agents will tend to converge towards certain areas of the space because of their learning processes. As a result, an agent's perception of the search space may benefit if computed over multiple rewards at early stages of learning, but additional rewards have little impact towards the end. We thus suggest that agents should be lenient with their teammates: ignore many of the low rewards initially, and fewer rewards as learning progresses. We demonstrate the benefit of lenience in a cooperative co-evolution algorithm and in a new reinforcement learning algorithm.

Concurrent learning is a form of cooperative multiagent learning in which each agent has an independent learning process and little or no control over its teammates' actions. In such learning algorithms, an agent's perception of the joint search space depends on the reward received by both agents, which in turn depends on the actions currently chosen by the other agents. The agents will tend to converge towards certain areas of the space because of their learning processes. As a result, an agent's perception of the search space may benefit if computed over multiple rewards at early stages of learning, but additional rewards have little impact towards the end. We thus suggest that agents should be lenient with their teammates: ignore many of the low rewards initially, and fewer rewards as learning progresses. We demonstrate the benefit of lenience in a cooperative co-evolution algorithm and in a new reinforcement learning algorithm.