The design of a complex system warrants a compositional methodology, i.e., composing simple components to obtain a larger system that exhibits their collective behavior in a meaningful way. We propose an automaton-based paradigm for compositional design of such systems where an action is accompanied by one or more preferences. At run-time, these preferences provide a natural fallback mechanism for the component, while at design-time they can be used to reason about the behavior of the component in an uncertain physical world. Using structures that tell us how to compose preferences and actions, we can compose formal representations of individual components or agents to obtain a representation of the composed system. We extend Linear Temporal Logic with two unary connectives that reflect the compositional structure of the actions, and show how it can be used to diagnose undesired behavior by tracing the falsification of a specification back to one or more culpable components.
We are interested in systems of cyber-physical agents that operate in unpredictable, possibly hostile, environments using locally obtainable information. How can we specify robust agents that are able to operate alone and or in cooperation with other agents? What properties are important? How can they be verified? In this tutorial we describe a framework called Soft Agents, formalized in the Maude rewriting logic system. Features of the framework include: explicit representation of the physical state as well as the cyber perception of this state; robust communication via sharing of partially ordered knowledge, and robust behavior based on soft constraints. Using Maude functionality, the soft agent framework supports experimenting with, formally testing, and reasoning about specifications of agent systems. The tutorial begins with a discussion of desiderata for soft agent models. Use of the soft agent framework for specification and formal analysis of agent systems illustrated in some detail by a case-study involving simple patrolling bots. A more complex case study involving surveillance drones is also discussed. In this paper we introduce constraint automata and propose them as an operational model for Reo, an exogenous coordination language for compositional construction of component connectors based on a calculus of channels. By providing composition operators for constraint automata and defining notions of equivalence and refinement relations for them, this paper covers the foundations for building tools to address concerns such as the automated construction of the automaton for a given component connector, equivalence checking or containment checking of the behavior of two given connectors, and verification of coordination mechanisms. We extend Constraint Automata by replacing boolean constraints with semiring-based soft constraints. The obtained general formal tool can be used to represent preference-based and similarity-based queries, which allow a user more freedom in choosing the behavior of the service to finally use, among all possible choices. A user states his preferences through a “soft” query, and obtains results that satisfy this query with different levels of preference. The soft requirements may involve a parameter data of the service operations, or the (names of the) operations themselves. Moreover, we introduce a first implementation of the search procedure by using declarative (soft) Constraint Programming. htmlabstractA formal description of a Cyber-Physical system should include a rigorous specification of the computational and physical components involved, as well as their interaction. Such a description, thus, lends itself to a compositional model where every module in the model specifies the behavior of a (computational or physical) component or the interaction between different components. We propose a framework based on Soft Constraint Automata that facilitates the component-wise description of such systems and includes the tools necessary to compose subsystems in a meaningful way, to yield a description of the entire system. Most importantly, Soft Constraint Automata allow the description and composition of components’ preferences as well as environmental constraints in a uniform fashion. We illustrate the utility of our framework using a detailed description of a patrolling robot, while highlighting methods of composition as well as possible techniques to employ them. We are interested in principles for designing and building open distributed systems consisting of multiple cyber-physical agents, specifically, where a coherent global view is unattainable and timely consensus is impossible. Such agents attempt to contribute to a system goal by making local decisions to sense and effect their environment based on local information. In this paper we propose a model, formalized in the Maude rewriting logic system, that allows experimenting with and reasoning about designs of such systems. Features of the model include communication via sharing of partially ordered knowledge, making explicit the physical state as well as the cyber perception of this state, and the use of a notion of soft constraints developed by Martin Wirsing and his team to specify agent behavior. The paper begins with a discussion of desiderata for such models and concludes with a small case study to illustrate the use of the modeling framework. Unmanned aerial vehicles (UAVs), a.k.a. drones, are becoming increasingly popular due to great advancements in their control mechanisms and price reduction. UAVs are being used in applications such as package delivery, plantation and railroad track monitoring, where UAVs carry out tasks in an automated fashion. Devising how UAVs achieve a task is challenging as the environment where UAVs are deployed is normally unpredictable, for example, due to winds. Formal methods can help engineers to specify flight strategies and to evaluate how well UAVs are going to perform to achieve a task. This paper proposes a formal framework where engineers can raise the confidence in their UAV specification by using symbolic, simulation and statistical and model checking methods. Our framework is constructed over three main components: the behavior of UAVs and the environment are specified in a formal executable language; the UAV’s physical model is specified by a simulator; and statistical model checking algorithms are used for the analysis of system behaviors. We demonstrate the effectiveness of our framework by means of several scenarios involving multiple drones.