47 citations · 77 across the 14 of their papers we have counts for
17 papers
ResonAct: Streaming Metrics for Runtime Diagnosis and Self-Healing in Multi-Agent Systems
Tarun Chintada, Neelamadhav Gantayat, Ishaan Romil +3
Multi-agent systems (MAS) are increasingly used to automate enterprise workflows involving multiple specialized agents, external tools, and long-running task execution. Failures ma…
Automated Creation and Enrichment Framework for Improved Invocation of Enterprise APIs as Tools
Prerna Agarwal, Himanshu Gupta, Soujanya Soni +3
Recent advancements in Large Language Models (LLMs) has lead to the development of agents capable of complex reasoning and interaction with external tools. In enterprise contexts,…
A Framework for Testing and Adapting REST APIs as LLM Tools
Jayachandu Bandlamudi, Ritwik Chaudhuri, Neelamadhav Gantayat +5
Large Language Models (LLMs) are increasingly used to build autonomous agents that perform complex tasks with external tools, often exposed through APIs in enterprise systems. Dire…
Developing Guidelines for Functionally-Grounded Evaluation of Explainable Artificial Intelligence using Tabular Data
Mythreyi Velmurugan, Chun Ouyang, Yue Xu +3
Explainable Artificial Intelligence (XAI) techniques are used to provide transparency to complex, opaque predictive models. However, these techniques are often designed for image a…
AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data
Santosh Palaskar, Vijay Ekambaram, Arindam Jati +11
The efficiency of business processes relies on business key performance indicators (Biz-KPIs), that can be negatively impacted by IT failures. Business and IT Observability (BizITO…
Goal-Oriented Next Best Activity Recommendation using Reinforcement Learning
Prerna Agarwal, Avani Gupta, Renuka Sindhgatta +1
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 compl…
Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanisms
Bemali Wickramanayake, Zhipeng He, Chun Ouyang +3
In this paper, we address the "black-box" problem in predictive process analytics by building interpretable models that are capable to inform both what and why is a prediction. Pre…
Explainable AI Enabled Inspection of Business Process Prediction Models
Chun Ouyang, Renuka Sindhgatta, Catarina Moreira
Modern data analytics underpinned by machine learning techniques has become a key enabler to the automation of data-led decision making. As an important branch of state-of-the-art…
Developing a Fidelity Evaluation Approach for Interpretable Machine Learning
Mythreyi Velmurugan, Chun Ouyang, Catarina Moreira +1
Although modern machine learning and deep learning methods allow for complex and in-depth data analytics, the predictive models generated by these methods are often highly complex,…
Evaluating Explainable Methods for Predictive Process Analytics: A Functionally-Grounded Approach
Mythreyi Velmurugan, Chun Ouyang, Catarina Moreira +1
Predictive process analytics focuses on predicting the future states of running instances of a business process. While advanced machine learning techniques have been used to increa…
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models
Catarina Moreira, Yu-Liang Chou, Mythreyi Velmurugan +3
The use of sophisticated machine learning models for critical decision making is faced with a challenge that these models are often applied as a "black-box". This has led to an inc…
An Investigation of Interpretability Techniques for Deep Learning in Predictive Process Analytics
Catarina Moreira, Renuka Sindhgatta, Chun Ouyang +2
This paper explores interpretability techniques for two of the most successful learning algorithms in medical decision-making literature: deep neural networks and random forests. W…
Exploring Interpretability for Predictive Process Analytics
Renuka Sindhgatta, Chun Ouyang, Catarina Moreira
Modern predictive analytics underpinned by machine learning techniques has become a key enabler to the automation of data-driven decision making. In the context of business process…
Targeted Example Generation for Compilation Errors
Umair Z. Ahmed, Renuka Sindhgatta, Nisheeth Srivastava +1
We present TEGCER, an automated feedback tool for novice programmers. TEGCER uses supervised classification to match compilation errors in new code submissions with relevant pre-ex…
Joint Multi-Domain Learning for Automatic Short Answer Grading
Swarnadeep Saha, Tejas I. Dhamecha, Smit Marvaniya +3
One of the fundamental challenges towards building any intelligent tutoring system is its ability to automatically grade short student answers. A typical automatic short answer gra…
DeepProcess: Supporting business process execution using a MANN-based recommender system
Asjad Khan, Hung Le, Kien Do +4
Process-aware Recommender systems can provide critical decision support functionality to aid business process execution by recommending what actions to take next. Based on recent a…
Cognitive Coordination of Global Service Delivery
Lav R. Varshney, Shivali Agarwal, Yi-Min Chee +4
Formal coordination mechanisms are of growing importance as human-based service delivery becomes more globalized and informal mechanisms are no longer effective. Further it is beco…