Non-monotonic reasoning rules for energy efficiency

Abstract

Abstract. Conflicting rules and rules with exceptions are very common in natural language specification employed to describe the behaviour of devices operating in a real-world context. This is common exactly because those specifications are processed by humans, and humans apply common sense and strategic reasoning about those rules to resolve the conflicts. In this paper, we deal with the challenge of providing, step by step, a model of energy saving rule specification and processing methods that are used to reduce the consumptions of a system of devices, by preventing energy waste. We argue that a very promising non-monotonic approach to such a problem can lie upon Defeasible Logic, following therefore an approach that has shown success in the current literature about usage of this logic for conflict rule resolution and for human-computer interaction in complex systems. Starting with rules specified at an abstract level, but compatibly with the natural aspects of such a specification (including temporal and power absorption constraints), we provide a formalism that generates the extension of a basic Defeasible Logic, which corresponds to turned on or off devices.

“It Could Be Worse, It Could Be Raining”: Reliable Automatic Meteorological Forecasting for Holiday Planning

“…-----------+------------+-----------------+-----------------+-----------------+-----------------+-----------------+-------------+---- -----------+------------+-----------------+-----------------+-----------------+-----------------+-----------------+-------------+---- Since the pioneering studies [5,9,23] and further engineering investigations on the commercial solutions [24], a first attempt going in the same direction that we following in this paper appeared in the 1990s [13] and inspired many specialized studies [19,21,17,12,4,20,10,11]. The ontological approach and the usage of the Internet of Things have been applied to forecasting quite recently [1,18] and we acknowledge that the main technical inspirations of our framework trace back these works, whereas the main influences come from the usage of non-monotonic deduction systems for sensor-based applications (clearly related to the initial part of the forecasting process) [28,8], and non-monotonic reasoning [16,25,14,15].…”

Applications of Linear Defeasible Logic: combining resource consumption and exceptions to energy management and business processes

“…Some of the authors have already carried out an effort in the direction of applying defeasible logic to energy management, in [6,27]. This complemented the investigations on applying logic and machine learning to energy management [5,4,28].…”

“…Towards a more logical viewpoint support to the decision can be obtained by using defeasible logic, as shown in particular in [6,27]. Starting from that concept, consisting in the usage of defeasible methods to represent different, possibly conflicting rules to save energy in home, home-office, small office contexts, we can apply the concept of linear defeasible logic to analogous contexts, enhancing the representation with a novel correspondence.…”

Diagnostics as a Reasoning Process: From Logic Structure to Software Design

“…In [14], [15], [16], [17], [18], [37], some of us have investigated the use of Defeasible Logic as a means for managing data coming from external sources and validated by means of data mining methods. Non-monotonic reasoning accommodates conclusions when dealing with potential conflicts.…”