Goal-Oriented Next Best Activity Recommendation using Reinforcement Learning

Recommending a sequence of activities for an ongoing case requires that the recommendations conform to the underlying business process and meet the performance goal of either completion time or process outcome. Existing work on next activity prediction can predict the future activity but cannot provide guarantees of the prediction being conformant or meeting the goal. Hence, we propose a goal-oriented next best activity recommendation. Our proposed framework uses a deep learning model to predict the next best activity and an estimated value of a goal given the activity. A reinforcement learning method explores the sequence of activities based on the estimates likely to meet one or more goals. We further address a real-world problem of multiple goals by introducing an additional reward function to balance the outcome of a recommended activity and satisfy the goal. We demonstrate the effectiveness of the proposed method on four real-world datasets with different characteristics. The results show that the recommendations from our proposed approach outperform in goal satisfaction and conformance compared to the existing state-of-the-art next best activity recommendation techniques

Hi, how can I help you?: Automating enterprise IT support help desks

Question answering is one of the primary challenges of natural language understanding. In realizing such a system, providing complex long answers to questions is a challenging task as opposed to factoid answering as the former needs context disambiguation. The different methods explored in the literature can be broadly classified into three categories namely: 1) classification based, 2) knowledge graph based and 3) retrieval based. Individually, none of them address the need of an enterprise wide assistance system for an IT support and maintenance domain. In this domain the variance of answers is large ranging from factoid to structured operating procedures; the knowledge is present across heterogeneous data sources like application specific documentation, ticket management systems and any single technique for a general purpose assistance is unable to scale for such a landscape. To address this, we have built a cognitive platform with capabilities adopted for this domain. Further, we have built a general purpose question answering system leveraging the platform that can be instantiated for multiple products, technologies in the support domain. The system uses a novel hybrid answering model that orchestrates across a deep learning classifier, a knowledge graph based context disambiguation module and a sophisticated bag-of-words search system. This orchestration performs context switching for a provided question and also does a smooth hand-off of the question to a human expert if none of the automated techniques can provide a confident answer. This system has been deployed across 675 internal enterprise IT support and maintenance projects.Comment: To appear in IAAI 201

An efficient routing tree construction algorithm with buffer insertion, wire sizing and obstacle considerations

In this thesis, we present a fast algorithm to construct a performance driven routing tree with simultaneous buffer insertion and wire sizing in the presence of wire and buffer obstacles. Recently several algorithms like Ptree, Stree, Sptree, and graph-RTBW have been published addressing the routing tree construction problem. But all these algorithms are slow and not scalable. Here we present an algorithm which is fast and scalable with problem size. The main idea of algorithm is to specify some important high-level features of the whole routing tree so that it can be broken down into several components. We apply stochastic search to find the best specification. Since we need very few high-level features to evaluate a routing tree, the size of stochastic search space is small which can be searched in very less time. The solutions for the components are either pre-generated and stored in lookup tables, or generated by extremely fast algorithms whenever needed. Since, the solutions of the components can be constructed efficiently, we can construct and evaluate the whole routing tree efficiently for each specification. Experimental results show that, for trees of moderate size, our algorithm is at least several hundred times faster than the recently proposed algorithms, Sptree and graph-RTBW, with not much difference in delay and resource consumption.</p

Tool for Automated Tax Coding of Invoices

Accounts payable refer to the practice where organizations procure goods and services on credit which need to be reimbursed to the vendors in due time. Once the vendor raises an invoice, it undergoes through a complex process before the final payment. In this process, tax code determination is one of the most challenging steps, which determines the tax to be levied and directly influences the amount payable to a vendor. This step is also very important from a regulatory compliance standpoint. However, it is error-prone, labor (resource) intensive, and needs regular training of the resources as it is done manually. Further, an error in the tax code determination induces penalties on the organization. Automatically arriving at a tax-code for a given product accurately and efficiently is a daunting task. To address this problem, we present an automated end-to-end system for tax code determination which can either be used as a standalone application or can be integrated into an existing invoice processing workflow. The proposed system determines the most relevant tax code for an invoice using attributes such as item description, vendor details, shipping and delivery location. The system has been deployed in production for a multinational consumer goods company for more than 6 months. It has already processed more than 22k items with an accuracy of more than 94% and high confidence prediction accuracy of around 99.54%. Using this system, approximately 73% of all the invoices require no human intervention

Retiming with interconnect and gate delay

In this paper, we study the problem of retiming of sequential circuits with both interconnect and gate delay. Most retiming algorithms have assumed ideal conditions for the non-logical portions of the data paths, which are not sufficiently accurate to be used in high performance circuits today. In our modeling, we assume that the delay of a wire is directly proportional to its length. This assumption is reasonable since the quadratic component of a wire delay is significantly smaller than its linear component when the more accurate Elmore delay model is used. A simple experiment is conducted to illustrate the validity of this assumption. We present two approaches to solve this problem, both of which have polynomial time complexity. The first one can compute the optimal clock period while the second one is an improvement over the first one in terms of practical applicability. The second approach gives solutions very close to the optimal (0.13% more than the optimal on average) but in a much shorter runtime. A circuit with more than 22K gates and 32K wires can be optimally retimed in 83.56 seconds by a PC with an 1.8GHz Intel Xeon processor.