The factory floor is a marvel of automation. With a press of a button, the whole place can seem to run itself. But although today's factories use automated workflows, process change is still mostly manual. When demands arise in an industrial environment, managers and engineers must interrupt the automation to update the processes that make the machines go. Now, thanks to machine learning algorithms, it's becoming possible for smart software to scrutinize data from a variety of sources -- sensors on machines or changes in supply chains, for instance -- and redesign processes in real time.
Artificial intelligence can be an important tool for collecting as well as interpreting business related data. Currently around 45 companies specialize in AI for functions such as recommendations, search, multichannel marketing, merchandising, and conversational commerce. Nothing works better for accumulating and making sense of all the data than machine learning, or AI. When automating aspects of a retail business, AI is able to provide an intelligent as well as a speedy solution, including self-adapting algorithms which can show a company's patterns of behavior which would otherwise be hidden from humans reading the data on their own. From there you have a starting point for predicting patterns of behavior and interaction which drive the most customer interaction, or purchasing.
In this paper we present a neural-symbolic system for monitoring traces of observations in sofware systems. To this end, we define an algorithm that translates a RuleR rule-based monitoring system (RS) into a rule-based neural network system (RNNS). We then show how the RNNS can perform trace monitoring effectively and analyze its performance, reporting promising preliminary results. Finally, we discuss how network learning could be used within RNNS to embed the system into a framework for iterative verification and model adaptation. It is hoped that a tight integration of verification and adaptation within the neural-symbolic approach will help support the development of self-adapting, self-healing systems.
We investigate a theory for Value of Information (VoI) with respect to the Internet of Things (IoT) and IoT's intrinsic Artificial Intelligence (AI). In an environment of ubiquitous computing and information, information's value takes on a new dimension. Moreover, when the system in which such a volume of information exists is itself intelligent, the ability to elicit value, in context, will be more complicated. Classi- cal economic theory describes the relationship between value and volume which, though moderated by demand, is highly correlated. In an environment where information is plentiful such as the IoT, the intrinsic intelligence in the system will be a dominant moderator of demand (e.g.
Oddi, Angelo (Institute of Cognitive Science and Technology, CNR) | Rasconi, Riccardo (Institute of Cognitive Science and Technology, CNR) | Cesta, Amedeo (Institute of Cognitive Science and Technology, CNR) | Smith, Stephen F. (Carnegie Mellon University)
This paper provides an analysis of the efficacy of a known iterative improvement meta-heuristic approach from the AI area in solving the Blocking Job Shop Scheduling Problem (BJSSP) class of problems. The BJSSP is known to have significant fallouts on practical domains, and differs from the classical Job Shop Scheduling Problem (JSSP) in that it assumes that there are no intermediate buffers for storing a job as it moves from one machine to another; according to the BJSSP definition, each job has to wait on a machine until it can be processed on the next machine. In our analysis, two specific variants of the iterative improvement meta-heuristic are evaluated: (1) an adaptation of an existing scheduling algorithm based on the Iterative Flattening Search and (2) an off-the-shelf optimization tool, the IBM ILOG CP Optimizer, which implements Self-Adapting Large Neighborhood Search. Both are applied to a reference benchmark problem set and comparative performance results are presented. The results confirm the effectiveness of the iterative improvement approach in solving the BJSSP; both variants perform well individually and together succeed in improving the entire set of benchmark instances.