Connected and autonomous vehiles (CAVs) require an effective cooperative action planning strategy in an emergency situation. Monte Carlo Tree Search (MCTS) is a promising planning technique for such problems with large state spaces. However, traditional MCTS-based techniques do not scale well with the number of vehicles. In this paper, we present a novel MCTS-based cooperative action planning algorithm for CAV s driving in a coalition formation. Our proposed algorithm improves the reliability and the scalability of M CTS. Explicit communication is used to ensure that mitigation action plans chosen by the CAVs are conflict-free when possible. We perform the evaluation of the proposed algorithm in a large scale multi-agent based traffic simulation system. Our simulated experiments show that our approach improves upon current state-of-the-art centralized and decentralized algorithms.

In this paper we discuss a multi-agent cooperative approach for decentralized, coordinated traffic systems. Intersection controllers are equipped with agents, i.e., autonomous software systems which are capable of communicating and cooperating with one another to achieve an individual or global goal. Our approach is based on real-world traffic parameters and constraints, and is meant to be implemented in existing traffic systems with minimal changes. Experimental results show that the agent-based approach outperforms the traditional pre-timed and actuated modes when traffic is heavy.

In this paper we present a model for an Agent-based intelligent Transportation System (ATS). In ATS, the traffic environment is partitioned into areas controlled by specialized agents called service managers. These agents are supervised by a traffic manager agent whose responsibility is to identifying global traffic management strategies. Intersections and vehicles are also equipped with agents. ATS is based on the premises that traffic management is the result of interactions between the various types of agents. We give an overview of MATISSE 2.0, a large-scale multi-agent based traffic simulation platform developed to test traffic scenarios in ATS. We discuss the underlying structure of the traffic network in MATISSE and provide a detailed discussion on the atomic network re-organization operations, i.e., incoming traffic flow elimination, road reversal and crossing elimination. Unlike many existing simulators which execute pre-defined network strategies, MATISSE allows ATS agents to select and execute network re-organization operations dynamically at run-time. The simulation of an evacuation scenario in a traffic network consisting of 384 roads and 64 intersections shows that the traffic re-organization operations improve evacuation time but are not be optimal.

In this paper we present DIVAs 4.0, a framework that supports the development of large-scale agent-based simulation systems where agents are situated in open environments. DIVAs includes high-level abstractions for the definition of agents and open environments, a micro kernel for the management of the simulation workflow, domain-specific libraries for the rapid development of simulations, and reusable, extendable components for the control and visualization of simulations. We illustrate the use of DIVAs through the development of a simple simulator where virtual agents are situated in a virtual city.

Abstract Graphical notations are widely used for system specification. The usefulness of these notations depends primarily on their readability. Hence, automatic methods are needed to obtain efficient and understandable graphical representations of requirements. In this paper, we present an algorithm that automatically generates layouts of statecharts. We assume that relevant information is stored in a structure that we call a decomposition tree, and we draw the graph that models a statechart in a hierarchical fashion. Our approach excludes diagrams with inter‐level transitions. Copyright © 2001 John Wiley & Sons, Ltd.

Given a software library whose entries are represented by formal specifications, we distinguish between two retrieval procedures: exact retrieval, whereby, given a query K, we identify all the library components that are correct with respect to K; approximate retrieval, which is invoked when exact retrieval fails, and identifies the library components that minimize adaptation effort. To this effect, we define four measures of functional distance between specifications, and discuss algorithms that minimize these measures over a set of components; then we discuss whether these measures can be used to predict adaptation effort.

Software libraries are repositories which contain software components; as such, they represent a precious resource for the software engineer. As software libraries grow in size, it becomes increasingly difficult to maintain adequate precision and recall with informal retrieval algorithms. In this paper, we discuss the design and implementation of a storage and retrieval structure for software components that is based on formal specifications and on the refinement ordering between specifications.

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We discuss our experience tvith integrating three key ideas of softurare engineen"ng into our data structures course: formal specifications, information hiding, and bottom up programming.