Abstract

Models of consensus are used to manage multiple agent systems in order to choose between different recommendations provided by the system. It is assumed that there is a central agent that solicits recommendations or plans from other agents. That agent the n determines the consensus of the other agents, and chooses the resultant consensus recommendation or plan. Voting schemes such as this have been used in a variety of domains, including air traffic control. This paper uses an analytic model to study the use of consensus in multiple agent systems. The binomial model is used to study the probability that the consensus judgment is correct or incorrect. That basic model is extended to account for both different levels of agent competence and unequal prior odds. The analysis of that model is critical in the investigation of multiple agent systems, since the model leads us to conclude that in some cases consensus judgment is not appropriate. In addition, the results allow us to determine how many agents should be used to develop consensus decisions, which agents should be used to develop consensus decisions and under which conditions the consensus model should be used.

Abstract

In this paper, an agent-based model is presented to test the feasibility of different configurations of Demand Responsive Shared Transport (DRST) services in a real context. DRST services provide “just-in-time” mobility solutions by dynamically assigning a fleet of vehicles to passenger booking requests taking advantages of Information and Communication Technologies. First results show the impact of different route choice strategies on the system performance and can be useful to help the planning and designing of such services.

Abstract

International audience; Urban transportation of next decade is expected to be disrupted by Autonomous Mobility on Demand (AMoD): AMoD providers will collect ride requests from users and will dispatch a fleet of autonomous vehicles to satisfy requests in the most efficient way. Differently from current ride sharing systems, in which driver behavior has a clear impact on the system, AMoD systems will be exclusively determined by the dispatching logic. As a consequence, a recent interest in the Operations Research and Computer Science communities has focused on this control logic. The new propositions and methodologies are generally evaluated via simulation. Unfortunately, there is no simulation platform that has emerged as reference, with the consequence that each author uses her own custom-made simulator, applicable only in her specific study, with no aim of generalization and without public release. This slows down the progress in the area as researchers cannot build on each other’s work and cannot share, reproduce and verify the results. The goal of this paper is to present AMoDSim, an open-source simulation platform aimed to fill this gap and accelerate research in future ride sharing system

Abstract

Optimal operation of a country's air transport infrastructure plays a major role in the economic development of nations. Due to the increasing use of air transportation in today's world, flights' boarding times have become a concern for both airlines and airports, thus the importance of knowing beforehand how changes in flights demand parameters and physical airport layout will affect passengers flow and boarding times. This paper presents a pedestrian modeling study in which a national airport passenger flow was analyzed. The study was conducted at Vanguardia National Airport in Villavicencio, Meta, Colombia. Different effects of structural changes are shown and provide judging elements for decision makers regarding passenger traffic in airport design. Comment: 8 pages, submitted to LACCEI 2016

Abstract

We present a new Q-function operator for temporal difference (TD) learning methods that explicitly encodes robustness against significant rare events (SRE) in critical domains. The operator, which we call the $\\kappa$-operator, allows to learn a robust policy in a model-based fashion without actually observing the SRE. We introduce single- and multi-agent robust TD methods using the operator $\\kappa$. We prove convergence of the operator to the optimal robust Q-function with respect to the model using the theory of Generalized Markov Decision Processes. In addition we prove convergence to the optimal Q-function of the original MDP given that the probability of SREs vanishes. Empirical evaluations demonstrate the superior performance of $\\kappa$-based TD methods both in the early learning phase as well as in the final converged stage. In addition we show robustness of the proposed method to small model errors, as well as its applicability in a multi-agent context. Comment: AAMAS 2019

Abstract

International audience; Cardio-pulmonary arrest is a common emergency situation causing over 400,000 deaths per year, more than a 1000 per day, in the USA alone. The goal of this work is to develop an agent based computer simulator that will allow trainers to experiment with different communication protocols, such as those found in air traffic control. This paper describes the first step in designing the simulator development. The design is based on an analysis of communications during real life training simulations using the FIPA standard categories.

Abstract

The development and deployment of Autonomous Vehicles (AVs) on our roads is not only realistic in the near future but can also bring significant benefits. In particular, it can potentially solve several problems relating to vehicles and traffic, for instance: (i) possible reduction of traffic congestion, with the consequence of improved fuel economy and reduced driver inactivity; (ii) possible reduction in the number of accidents, assuming that an AV can minimise the human errors that often cause traffic accidents; and (iii) increased ease of parking, especially when one considers the potential for shared AVs. In order to deploy an AV there are significant steps that must be completed in terms of hardware and software. As expected, software components play a key role in the complex AV system and so, at least for safety, we should assess the correctness of these components. In this paper, we are concerned with the high-level software component(s) responsible for the decisions in an AV. We intend to model an AV capable of navigation; obstacle avoidance; obstacle selection (when a crash is unavoidable) and vehicle recovery, etc, using a rational agent. To achieve this, we have established the following stages. First, the agent plans and actions have been implemented within the Gwendolen agent programming language. Second, we have built a simulated automotive environment in the Java language. Third, we have formally specified some of the required agent properties through LTL formulae, which are then formally verified with the AJPF verification tool. Finally, within the MCAPL framework (which comprises all the tools used in previous stages) we have obtained formal verification of our AV agent in terms of its specific behaviours. For example, the agent plans responsible for selecting an obstacle with low potential damage, instead of a higher damage obstacle (when possible) can be formally verified within MCAPL. We must emphasise that the major goal (of our present approach) lies in the formal verification of agent plans, rather than evaluating real-world applications. For this reason we utilised a simple matrix representation concerning the environment used by our agent. Comment: In Proceedings FVAV 2017, arXiv:1709.02126

Abstract

Recent advances in wireless technologies have enabled many new applications in Intelligent Transportation Systems (ITS) such as collision avoidance, cooperative driving, congestion avoidance, and traffic optimization. Due to the vulnerable nature of wireless communication against interference and intentional jamming, ITS face new challenges to ensure the reliability and the safety of the overall system. In this paper, we expose a class of stealthy attacks -- Stuck in Traffic (SiT) attacks -- that aim to cause congestion by exploiting how drivers make decisions based on smart traffic signs. An attacker mounting a SiT attack solves a Markov Decision Process problem to find optimal/suboptimal attack policies in which he/she interferes with a well-chosen subset of signals that are based on the state of the system. We apply Approximate Policy Iteration (API) algorithms to derive potent attack policies. We evaluate their performance on a number of systems and compare them to other attack policies including random, myopic and DoS attack policies. The generated policies, albeit suboptimal, are shown to significantly outperform other attack policies as they maximize the expected cumulative reward from the standpoint of the attacker.

Abstract

The increasing use of private vehicles for transportation in cities results in a growing demand for parking space and road network capacity. In many densely populated urban areas, however, the capacity of existing infrastructure is insufficient and extremely difficult to expand. Mobility-on-demand systems have been proposed as a remedy to the problem of limited parking space because they are able to satisfy the existing transportation demand with fewer shared vehicles and consequently require less parking space. Yet, the impact of large-scale vehicle sharing on traffic patterns is not well understood. In this work, we perform a simulation-based analysis of consequences of a hypothetical deployment of a large-scale station-based mobility-on-demand system in Prague and measure the traffic intensity generated by the system and its effects on the formation of congestion. We find that such a mobility-on-demand system would lead to significantly increased total driven distance and it would also increase levels of congestion due to extra trips without passengers. In fact, 38% kilometers traveled in such an MoD system would be driven empty. Comment: accepted for ITSC 2017

Abstract

the number of personal computing and IoT devices grows rapidly, so does the amount of computational power that is available at the edge. Since many of these devices are often idle, there is a vast amount of computational power that is currently untapped, and which could be used for outsourcing computation. Existing solutions for harnessing this power, such as volunteer computing (e.g., BOINC), are centralized platforms in which a single organization or company can control participation and pricing. By contrast, an open market of computational resources, where resource owners and resource users trade directly with each other, could lead to greater participation and more competitive pricing. To provide an open market, we introduce MODiCuM, a decentralized system for outsourcing computation. MODiCuM deters participants from misbehaving-which is a key problem in decentralized systems-by resolving disputes via dedicated mediators and by imposing enforceable fines. However, unlike other decentralized outsourcing solutions, MODiCuM minimizes computational overhead since it does not require global trust in mediation results. We provide analytical results proving that MODiCuM can deter misbehavior, and we evaluate the overhead of MODiCuM using experimental results based on an implementation of our platform.