Finite-state abstractions (a.k.a. symbolic models) present a promising avenue for the formal verification and synthesis of controllers in continuous-space control systems. These abstractions provide simplified models that capture the fundamental behaviors of the original systems. However, the creation of such abstractions typically relies on the availability of precise knowledge concerning system dynamics, which might not be available in many real-world applications. In this work, we introduce an innovative, data-driven, and compositional approach to generate finite abstractions for interconnected systems that consist of discrete-time control subsystems with unknown dynamics. These subsystems interact through an unknown static interconnection map. Our methodology for abstracting the interconnected system involves constructing abstractions for individual subsystems and incorporating an abstraction of the interconnection map.

Abstraction-based techniques are an attractive approach for synthesizing correct-by-construction controllers to satisfy high-level temporal requirements. A main bottleneck for successful application of these techniques is the memory requirement, both during controller synthesis and in controller deployment. We propose memory-efficient methods for mitigating the high memory demands of the abstraction-based techniques using neural network representations. To perform synthesis for reach-avoid specifications, we propose an on-the-fly algorithm that relies on compressed neural network representations of the forward and backward dynamics of the system. In contrast to usual applications of neural representations, our technique maintains soundness of the end-to-end process. To ensure this, we correct the output of the trained neural network such that the corrected output representations are sound with respect to the finite abstraction. For deployment, we provide a novel training algorithm to find a neural network representation of the synthesized controller and experimentally show that the controller can be correctly represented as a combination of a neural network and a look-up table that requires a substantially smaller memory. We demonstrate experimentally that our approach significantly reduces the memory requirements of abstraction-based methods. For the selected benchmarks, our approach reduces the memory requirements respectively for the synthesis and deployment by a factor of $1.31\times 10^5$ and $7.13\times 10^3$ on average, and up to $7.54\times 10^5$ and $3.18\times 10^4$. Although this reduction is at the cost of increased off-line computations to train the neural networks, all the steps of our approach are parallelizable and can be implemented on machines with higher number of processing units to reduce the required computational time.

We develop a methodology to model and verify Regarding the second limitation, proposals have been put open multi-agent systems (OMAS), where agents forward to consider a set of objects that vary at design time; may join in or leave at run time. Further, we specify the set of agents is normally considered to be finite in each properties of interest on OMAS in a variant of firstorder system run. This is a sensible assumption in many scenarios, temporal-epistemic logic, whose characterising but there are applications of MAS (e.g., e-commerce, smart features include epistemic modalities indexed grids) where an unbounded number of agents may freely enter to individual terms, interpreted on agents appearing and leave the system at run time. There is, therefore, at a given state. This formalism notably allows a need to account for the unbounded and possibly infinite to express group knowledge dynamically. We study agents joining in or leaving an open MAS. In this setting it the verification problem of these systems and show is still of interest to reason about their evolution and what that, under specific conditions, finite bisimilar abstractions they know individually and collectively. For example, in an can be obtained.

Artifact systems are a novel paradigm for specifying and implementing business processes described in terms of interacting modules called artifacts. Artifacts consist of data and lifecycles, accounting respectively for the relational structure of the artifacts states and their possible evolutions over time. In this paper we put forward artifact-centric multi-agent systems, a novel formalisation of artifact systems in the context of multi-agent systems operating on them. Differently from the usual process-based models of services, we give a semantics that explicitly accounts for the data structures on which artifact systems are defined. We study the model checking problem for artifact-centric multi-agent systems against specifications expressed in a quantified version of temporal-epistemic logic expressing the knowledge of the agents in the exchange. We begin by noting that the problem is undecidable in general. We identify a noteworthy class of systems that admit bisimilar, finite abstractions. It follows that we can verify these systems by investigating their finite abstractions; we also show that the corresponding model checking problem is EXPSPACE-complete. We then introduce artifact-centric programs, compact and declarative representations of the programs governing both the artifact system and the agents. We show that, while these in principle generate infinite-state systems, under natural conditions their verification problem can be solved on finite abstractions that can be effectively computed from the programs. We exemplify the theoretical results here pursued through a mainstream procurement scenario from the artifact systems literature.

Artifact systems are a novel paradigm for specifying and implementing business processes described in terms of interacting modules called artifacts. Artifacts consist of data and lifecycles, accounting respectively for the relational structure of the artifacts' states and their possible evolutions over time. In this paper we put forward artifact-centric multi-agent systems, a novel formalisation of artifact systems in the context of multi-agent systems operating on them. Differently from the usual process-based models of services, the semantics we give explicitly accounts for the data structures on which artifact systems are defined. We study the model checking problem for artifact-centric multi-agent systems against specifications written in a quantified version of temporal-epistemic logic expressing the knowledge of the agents in the exchange. We begin by noting that the problem is undecidable in general. We then identify two noteworthy restrictions, one syntactical and one semantical, that enable us to find bisimilar finite abstractions and therefore reduce the model checking problem to the instance on finite models. Under these assumptions we show that the model checking problem for these systems is EXPSPACE-complete. We then introduce artifact-centric programs, compact and declarative representations of the programs governing both the artifact system and the agents. We show that, while these in principle generate infinite-state systems, under natural conditions their verification problem can be solved on finite abstractions that can be effectively computed from the programs. Finally we exemplify the theoretical results of the paper through a mainstream procurement scenario from the artifact systems literature.

We explore the paradigm of artifact-centric systems from a knowledge-based perspective. We provide a semantics based on interpreted-systems to interpret a first-order temporal- epistemic language with identity in a multi-agent setting. We consider the model checking problem for this language and provide abstraction results. We isolate a natural subclass of artifact-systems for which the model checking problem is decidable. We give an upper bound on the complexity of the model checking problem.