Projects

BatCAT is a research and innovation project funded by the EU's Horizon Europe programme. The project is a collaboration between 18 partner organisations from 9 European countries, coordinated by NMBU.The project aims to create a digital twin for battery manufacturing by developing a cross-chemistry data space for two technologies: Li-ion and Li-S coin cells and redox flow batteries. The project will also address three challenges in digital manufacturing: Design, operation, and trust. BatCAT is closely connected to BIG-MAP and BATTERY 2030+, EOSC, EMMC, and OntoCommons, ensuring a community and industry uptake of the results.

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In SwarmIn the overall goal is to balance the WIP waves and flow factors by applying a production plant optimization featured with energy- and resource-efficiency parameters in an industry 4.0 setting as given with a production plant of, e.g., IFAT. These parameters will enrich the optimization procedure that builds upon a tightly interconnected high-level, combinatorial and a low-level swarm intelligence optimization that presents the project’s output in form of an artificial intelligence (AI)-based software library. In combinatorial optimization the focus lies on global production task planning, resource allocation, and cyber-physical system (CPS) configuration, yet based on abstract models of the production process chain to manage the complexity and uncertainty of industrial CPSs. In optimization using swarm intelligence, the algorithms are considered as local rules for the agents (heterogeneous swarm of CPSs and humans) performing the optimization from bottom-up. We design a novel mixed AI architecture, where the low-level optimization with swarm intelligence runs on local edges supported by retrofit sensors and local data reduction as input for the local decision rules, whereas high-level optimization is done centrally, e.g., as a cloud service.

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Automated planning, understood as “reasoning about acting'' is one of the oldest problems studied in Artificial Intelligence and has been successfully applied in many practical domains. Environments are described from the point of view of an agent, and its dynamics are captured by actions (under the control of the agent) and events (not under its control). Besides plan generation, formal symbolic specification of planning tasks is important for investigating their structure. Action reversibility, which is the concern of this project, refers to the possibility to revert effects of an action by (other) actions. In other words, after execution of a reversible action, there exists a sequence of actions that reverts the state of the environment to exactly the state before the reversible action was executed. The problem of determining reversibility of actions has been identified and tackled in a few previous works, but the existing approaches rely on either very general or quite restrictive frameworks. In the proposed project we propose to explore more of the middle ground, in particular domains represented using lifted representation and Fully Observable Non-deterministic (FOND) domains. By leveraging Answer Set Programming techniques, we believe that we will be able to construct a framework that will provide a good tradeoff between being general and efficient. There are several indications that ASP is particularly well-suited for determining reversible actions and events in lifted and FOND domains, which we intend to substantiate in the project. The contributions of the project will therefore be both theoretical and practical in nature, in the form of novel definitions of reversibility, a formal study of their computational properties, new algorithms, their implementation, and an experimental evaluation.

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