In this paper, we study the introduction of modal past temporal operators in Temporal Equilibrium Logic (TEL), an hybrid formalism that mixes linear-time modalities and logic programs interpreted under stable models a...
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We present a tool for compiling three problems from the Process Mining community into answer set programming: Log Generation, Conformance Checking, and Query Checking. For each problem, two versions are addressed, one...
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We present a tool for compiling three problems from the Process Mining community into answer set programming: Log Generation, Conformance Checking, and Query Checking. For each problem, two versions are addressed, one considering only the control-flow perspective and the other considering also the data perspective. The tool can support companies in analyzing their business processes;it is highly flexible and general, and can be easily modified to address other problems from Declarative Process Mining.
In temporal extensions of answer set programming (ASP) based on linear-time, the behaviour of dynamic systems is captured by sequences of states. While this representation reflects their relative order, it abstracts a...
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The SOSA c -Reasoner is a commonsense reasoning engine, implemented using answer set programming. It is designed to automatically generate IoT context knowledge, representing the capabilities of system devices, from a...
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The SOSA c -Reasoner is a commonsense reasoning engine, implemented using answer set programming. It is designed to automatically generate IoT context knowledge, representing the capabilities of system devices, from a simple smart scenario description. The inference engine is fed with knowledge about device types and generates knowledge according to two ontologies derived from the SOSA (Sensor, Observation, Sample, and Actuator) ontology. The SOSA c -Reasoner comprises two ASP rule modules: the basic and advanced inference modules, which perform reasoning with different objectives. Implemented with Potassco, the SOSA c -Reasoner effectively generates context knowledge within a reasonable timeframe. This significantly facilitates the task of modeling a highly valuable type of knowledge in intelligent environments, a task that traditionally involves manual efforts, is prone to errors, and consumes a significant amount of time.
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