This research explores the integration of the Digital Twins Definition Language (DTDL) with ASP Chef, a webbased platform for Answer Set Programming (ASP). The goal of the research is to take advantage of the capabilities of ASP Chef to query, analyze, and visualize digital twins. As a first step in this direction, we introduce a method for mapping DTDL-defined digital twin models into ASP facts. This approach enables a seamless transition from high-level digital twin specifications to declarative reasoning and interactive visualization, ofering a flexible framework for interpreting and exploring digital twin configurations.
composed of a series of ingredients, where each ingredient encapsulates a specific operation: grounding a logic program, solving it to compute answer sets, filtering results based on user-defined conditions, applying queries, generating new interpretations, or rendering output in visual form. This modular design allows users to construct complex logic-based workflows in a step-by-step manner, facilitating experimentation, compositional reasoning, and reuse of processing logic across diferent applications.
What sets ASP Chef apart is its ability to manage interpretation streams (i.e., sequences of partial or complete answer sets) through these pipelines. Rather than returning a static set of results, recipes can transform intermediate results, chain operations, and ultimately produce outputs that are either textual (e.g., ASP facts) or graphical (e.g., network visualizations). The visual layer is powered by templating systems such as Mustache, integrated with JavaScript visualization libraries like @vis.js/Network, enabling interactive graphical representations of logical models. The user interface of ASP Chef is entirely browser-based and designed for accessibility and ease of use, lowering the barrier for both newcomers and experts. Users do not need to install any external solvers or tools locally; the platform integrates standard ASP solvers such as clingo and handles grounding, solving, and visualization client-side. This makes it particularly suitable for educational contexts, rapid prototyping, and data-rich domains where logical models must be both computed and interpreted by humans.
The objective of this work is to enable the analysis and visualization of digital twins defined using DTDL within the declarative and visual framework ofered by ASP Chef. While DTDL provides a standardized, extensible way to model the structure and semantics of digital twins, defining interfaces, properties, relationships, and telemetry, its focus remains on representation rather than reasoning. By translating DTDL models into ASP facts, we can take advantage of the expressive power of ASP to perform complex queries, detect inconsistencies, infer implicit knowledge, and simulate behaviors over digital twin models. Integrating these two technologies allows for the creation of flexible reasoning pipelines where DTDL-defined twins serve as the data layer, and ASP Chef recipes provide the logic and visualization layers. In this approach, the declarative logic encoded in ASP can be used to filter or analyze digital twin instances. At the same time, the built-in support for templating and graph-based visualization in ASP Chef makes it possible to render digital twin networks in an interactive and humanfriendly form. This integration thus bridges high-level semantic modeling with logic-based analysis and visual feedback, empowering users to inspect, reason over, and communicate digital twin configurations more efectively. This integration is particularly relevant in contexts where explainability, rule-based control, or constraint checking over digital twins is essential. For example, in smart building scenarios, one may use DTDL to describe a the layout of a building, sensors, and control systems, and then use ASP to verify that sensor placement satisfies coverage constraints, or to detect redundant or missing components. In energy grids or manufacturing processes, logical rules may encode safety policies, energy eficiency conditions, or fault detection mechanisms that can be automatically applied to the representation of the digital twin.
Digital twins have gained significant attention in recent years as a paradigm for modeling, monitoring,
and controlling cyber-physical systems. DTDL, developed by Microsoft, has emerged as a standard for
describing digital twin interfaces in a structured and extensible way. It is widely used in industrial IoT
settings, particularly within the Azure Digital Twins platform. While DTDL focuses on the semantic
modeling of digital entities, it does not ofer built-in mechanisms for logical reasoning or constraint
checking. In contrast, ASP provides a formalism well-suited for representing complex knowledge
and deriving conclusions under non-monotonic reasoning. ASP has been successfully applied in
configuration [
On the visualization side, tools such as vis.js have been used extensively for interactive graph
visualization. ASP Chef [
In summary, while prior works have addressed model-based design, digital twin representation, or ASP reasoning in isolation, this paper contributes a novel integration that combines semantic modeling (via DTDL), logical reasoning (via ASP), and interactive visualization (via ASP Chef), ofering a complete pipeline for digital twin analysis and validation.
The goal of this research is to extend the integration of Digital Twins and Answer Set Programming by enabling full lifecycle reasoning and advanced analytics within ASP Chef. Building upon the current support for DTDL model parsing and visualization, we envision the following developments: • Automated Instance Management: support for the creation, validation, and retirement of digital twin instances based on declarative rules and contextual information (e.g., usage patterns, spatial or temporal conditions). • Temporal and Predictive Reasoning: incorporation of temporal extensions to ASP to allow reasoning over telemetry streams, identifying system dynamics, and enabling simulation or forecast of future behaviors. • Scalability and Distribution: adoption of distributed ASP strategies and modular representations to handle large-scale digital twin networks, particularly in domains such as smart agriculture, energy systems, and industrial IoT.
The overarching objective is to create a unified, logic-based framework that supports both structural and behavioral aspects of digital twin systems, ofering explainability, constraint checking, and decision support.
This research presents a preliminary result on our ongoing efort to extend ASP Chef with support for the Digital Twins Definition Language (DTDL). Our goal is to enable reasoning and visualization capabilities over digital twin models by integrating a widely adopted semantic modeling language with the declarative power of Answer Set Programming. To this end, we defined a mapping from DTDL constructs (such as interfaces, properties, telemetry, commands, and relationships) to ASP facts, capturing the structure and semantics of digital twin models in a logic-programmable format. This mapping has been implemented as a new operation within ASP Chef, which processes Base64encoded DTDL models and emits a corresponding set of ASP facts. The new functionality integrates seamlessly with other ASP Chef operations. It supports model querying, constraint validation, and the generation of interactive visualizations through templated configurations of front-end libraries such as @vis.js/Network. The approach is composable and declarative, aligning well with the recipe-based workflow typical of ASP Chef.
A current limitation of our work lies in the absence of support for the serialization of digital twin instances, that is, concrete entities conforming to DTDL models, along with their runtime data. Our goal is to enable full digital twin lifecycle support, from model ingestion to instance management and data-driven reasoning.
This section illustrates a use case involving the modeling of a vineyard as a system of interconnected digital twins. DTDL is used to define the digital counterparts of key physical components involved in viticulture. The goal is to demonstrate how a complex system composed of sensors, controllers, and monitoring devices can be translated into ASP facts for reasoning and visualization purposes.
The system is composed of five main DTDL interfaces: • Vineyard Property: represents the entire vineyard estate and acts as the central entry point of the model. • Pest Trap: defines smart traps used for pest monitoring. • Soil Moisture Sensor: describes environmental sensors installed in vineyard plots. • Irrigation Controller: models smart irrigation systems used to manage water distribution. • Grape Monitor: represents devices used to monitor grape development and maturity. These interfaces define properties, telemetry data, commands, and relationships. For example, the Vineyard Property model includes descriptive fields such as name, owner, totalArea, and spatial data (location), as well as relationships to vineyard plots, weather stations, and wineries.
To enable reasoning over digital twin data in ASP Chef, each DTDL model is mapped to a collection of ASP facts. This translation makes it possible to analyze the model declaratively, verifying constraints and inferring system-level properties.
A key part of the vineyard system lies in the relationships among components. The DTDL models define cardinality constraints and logical links between devices: • Vineyard Property may contain up to 100 Vineyard Plot instances. • Irrigation Controller can manage up to 10 plots. • Soil Moisture Sensor and Pest Trap devices are installed within individual plots. • Grape Monitor monitors a single plot and provides detailed telemetry.
These relationships are mapped into ASP facts using constructs like: 1 { data: { 2 nodes: [ 3 {{= {{f"{ id: '${I}', label: '${N}', group: '${T}' }"}} : node(T,I,N) }} ], 4 edges: [ 5 {{= {{f"{ from: '${F}', to: '${T}', label: '${L}', arrows: 'to' }"}} 6 : link(F,T,L) }} ] }, 7 options: { 8 nodes: { shape: "dot", size:20, font:{size:16}, borderWidth:2, shadow: true }, 9 edges: { width: 2, shadow: true }, 10 groups: { 11 interface: { font: { size: 24 }, size: 30, 12 color: { background: lightgreen, border: green } }, 13 property: { color: { background: yellow, border: orange } }, 14 telemetry: { color: { background: pink, border: red } }, 15 object: { shape: "hexagon", color: {background:"#d1c4e9",border: "#512da8"}}, 16 enum: { shape: "diamond", color: { background:"#fff9c4", border:"#fbc02d" }} 17 } } }
This structure supports the application of inference rules for validating instance data and ensuring conformance with the intended digital twin architecture.
The resulting ASP knowledge base allows for reasoning tasks such as: • Verifying that each Vineyard Plot is assigned a moisture sensor. • Ensuring that no Irrigation Controller exceeds its control limit. • Identifying unmonitored plots or plots with conflicting sensor assignments.
• Simulating scheduling decisions based on sensor telemetry (e.g., when to irrigate or harvest). For example, relationships specifying as target an interface model not provided in input can be easily identified with the following rule:
node(undefined_interface,T,T) :- target(_,T), not interface(T).
The obtained instance of node/3 can in turn be coupled with a Mustache template extending Figure 1 with the group undefined_interface: { font: { size: 24 }, size: 30,
color: { background: red, border: darkred } }, to highlight in red any undefined interface, as shown in Figure 2. A complete recipe is available at https://asp-chef.alviano.net/s/CILC2025/vineyard.
To realize our vision, several open issues must be addressed. At present, the framework supports only DTDL models, lacking mechanisms to process and reason over actual instances of digital twins that embody live operational data. Our first objective is to extend the pipeline to include the ingestion of JSON-formatted instances enriched with telemetry values and command states, translating them into ASP facts suitable for reasoning. This will require an extension of the current parsing component and the definition of an enriched vocabulary of facts, maintaining compatibility with the modular logic of ASP Chef.
An essential requirement is the semantic validation of instance data with respect to the DTDL schema from which it originates. This involves enforcing constraints on type conformance, cardinality, access permissions, and hierarchical relations. To this end, we take inspiration from the SHACL framework[15, 16], which provides a powerful means of validating RDF graphs against structural patterns. Our aim is to encode analogous validation logic declaratively in ASP, enabling formal and extensible verification rules grounded in the schema semantics.
Another significant limitation is the absence of temporal reasoning. Digital twin instances often involve time-stamped data and state evolution over time, aspects that cannot be adequately captured through purely static representations. We plan to adopt and adapt existing proposals for temporal extensions of ASP[9, 11], which ofer mechanisms for expressing fluents, actions, and temporal constraints. Such extensions are vital for tasks like history-aware validation, detection of behavioral anomalies, and simulation of future system trajectories.
We also intend to integrate predictive components based on statistical learning. Hybrid reasoning— combining symbolic logic with sub-symbolic models—has been shown to be efective in domains requiring both explainability and generalization. In particular, we plan to evaluate the use of time series forecasting tools such as TimeGPT[11] for complementing ASP-based pipelines with trend prediction capabilities. The modularity are key challenge as digital twin ecosystems become increasingly complex. The current framework must be extended with support for distributed reasoning, possibly using federated knowledge bases and modular program decomposition. Caching frequently accessed intermediate results and designing reusable components will be critical for maintaining performance and usability at scale.
Collectively, addressing these challenges will allow ASP Chef to evolve into a comprehensive and versatile platform for digital twin modeling, simulation, and validation, bridging semantic modeling, logical inference, and data-driven decision making in a unified pipeline.
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In the figure 2 we see a complex pattern of digital twins representing a vineyard. The main elements of the graph are the nodes representing the digital twins of: • Grape Monitor • Soil Mosture Sensor • Irrigation Controller • Pest Trap From each node come arcs representing the properties of the individual entities and the type of data used. Finally, it is possible to visually verify which of these expected properties, defined in the models, are not actually defined in the specific instances of the digital twin we are visualising. Visualisation also makes it possible to inspect complex properties and types: properties that refer to an object and not to a primitive type.