Foundations of Executable Temporal Logic. In many computer applications, including those of temporal reasoning, distributed computations and knowledge representations, the concept of time is of central importance. Multiple granularity of time also plays a critical role as not all events are necessarily defined over a uniform model of time. This project will develop the foundations of executable logical representations, supporting multiple granularity of time. This will allow system developers a ....Foundations of Executable Temporal Logic. In many computer applications, including those of temporal reasoning, distributed computations and knowledge representations, the concept of time is of central importance. Multiple granularity of time also plays a critical role as not all events are necessarily defined over a uniform model of time. This project will develop the foundations of executable logical representations, supporting multiple granularity of time. This will allow system developers access to powerful logical techniques in those applications. In the process, fundamental problems in modelling multiple granularity of time will be identified, and application-independent solutions to those problems will be provided.Read moreRead less
RichProlog, a System for Deducing, Inducing and Learning in the Declarative Programming Paradigm. The aim of the project is to contribute to bridge the gap between learning and logic, theoretically and practically. Our purpose is to extend considerably the scope of the declarative programming paradigm, and build a system that can be used to solve learning or discovery problems as encountered in Artificial Intelligence. The system will enable rapid prototyping when applied to problems involving d ....RichProlog, a System for Deducing, Inducing and Learning in the Declarative Programming Paradigm. The aim of the project is to contribute to bridge the gap between learning and logic, theoretically and practically. Our purpose is to extend considerably the scope of the declarative programming paradigm, and build a system that can be used to solve learning or discovery problems as encountered in Artificial Intelligence. The system will enable rapid prototyping when applied to problems involving deduction, induction, and nonmonotonic reasoning. We intend the system to become a standard tool for tackling a broad range of applications, and the underlying theory to provide new insights on the logical foundations of Artificial Intelligence.
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