98 research outputs found
Strategic Argumentation is NP-Complete
In this paper we study the complexity of strategic argumentation for dialogue
games. A dialogue game is a 2-player game where the parties play arguments. We
show how to model dialogue games in a skeptical, non-monotonic formalism, and
we show that the problem of deciding what move (set of rules) to play at each
turn is an NP-complete problem
onto plc an ontology driven methodology for converting plc industrial plants to iot
Abstract We present the new methodology ONTO-PLC to deliver software programs on system-on-chip or single-board computers used to control industrial plants, as substitutes for programmable logic control technologies. The methodology is ontology-driven based on the abstract description of the plant at a level in which the plant itself is viewed as a set of instruments, each instrument being a set of machineries coordinated in functional terms by a control system, formed by sensors and actuators, under the control of an abstract model of behavior delivered by means of an extended finite state machine
Changes to temporary norms
Normative systems accommodate temporary norms of several types, which can also be modified in different, and codified ways. In this paper we address the problem of modifying temporary norms that are represented by means of the combination of two known formalisms in the current literature. The framework evolves from a known one, which provides a system of norms at two distinct layers, and represents changes at the two layers as means to provide room for the codified change types. This results in four novel operators that anticipate and extend norms in two different combined ways, by preserving or not the effects of the norms in the period of time generated by the temporal modifications. We study these new oper- ators and show how they relate to the operators of annulment and abrogation analysed elsewhere
a simple algorithm for the lexical classification of comparable adjectives
Abstract Lexical classification is one of the most widely investigated fields in (computational) linguistic and Natural language Processing. Adjectives play a significant role both in classification tasks and in applications as sentiment analysis. In this paper a simple algorithm for lexical classification of comparable adjectives, called MORE (coMparable fORm dEtector), is proposed. The algorithm is efficient in time. The method is a specific unsupervised learning technique. Results are verified against a reference standard built from 80 manually annotated lists of adjective. The algorithm exhibits an accuracy of 76%
Applications of Linear Defeasible Logic: combining resource consumption and exceptions to energy management and business processes
Linear Logic and Defeasible Logic have been adopted to formalise different
features of knowledge representation: consumption of resources, and non
monotonic reasoning in particular to represent exceptions. Recently, a
framework to combine sub-structural features, corresponding to the consumption
of resources, with defeasibility aspects to handle potentially conflicting
information, has been discussed in literature, by some of the authors. Two
applications emerged that are very relevant: energy management and business
process management. We illustrate a set of guide lines to determine how to
apply linear defeasible logic to those contexts.Comment: In Proceedings DICE-FOPARA 2019, arXiv:1908.04478. arXiv admin note:
substantial text overlap with arXiv:1809.0365
On the use of learning-based forecasting methods for ameliorating fashion business processes: A position paper
The fashion industry is one of the most active and competitive markets in the
world, manufacturing millions of products and reaching large audiences every
year. A plethora of business processes are involved in this large-scale
industry, but due to the generally short life-cycle of clothing items,
supply-chain management and retailing strategies are crucial for good market
performance. Correctly understanding the wants and needs of clients, managing
logistic issues and marketing the correct products are high-level problems with
a lot of uncertainty associated to them given the number of influencing
factors, but most importantly due to the unpredictability often associated with
the future. It is therefore straightforward that forecasting methods, which
generate predictions of the future, are indispensable in order to ameliorate
all the various business processes that deal with the true purpose and meaning
of fashion: having a lot of people wear a particular product or style,
rendering these items, people and consequently brands fashionable. In this
paper, we provide an overview of three concrete forecasting tasks that any
fashion company can apply in order to improve their industrial and market
impact. We underline advances and issues in all three tasks and argue about
their importance and the impact they can have at an industrial level. Finally,
we highlight issues and directions of future work, reflecting on how
learning-based forecasting methods can further aid the fashion industry.Comment: 2nd International Workshop on Industrial Machine Learning @ ICPR 202
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