Knowledge-guided AI-native Adaptive Enterprise

A future enterprise will be a complex ecosystem (or system of systems) of socio-cyber-physical actors that operates in a dynamic uncertain environment. It will need to continue delivering its goals whilst dealing with unforeseen changes along multiple dimensions such as events opening new opportunities or constraining the existing ones or disruptions like pandemics, competitor actions, regulatory regime, law of the land, and technology advance/obsolescence.

These dynamics will play out at multiple levels, requiring a cohesive response across three key planes of enterprise, namely: the intent plane concerned with the purpose of the enterprise, the processes plane concerned with the processes needed to realise the purpose, and the organisational plane concerned with the organization of the (socio-cyber-physical) actors (and infrastructures) that enact the different processes. All of this facing continuously shrinking time windows. Changes may originate in one plane and ripple through to the other planes. This puts hitherto unseen demands on enterprises as regards responsive decision-making with partial information in the face of uncertainty and swift adaptation to support continuous transformation while optimizing stakeholder value. Given the increasing pervasiveness of software in enterprises, these demands translate on enterprise software systems as well.

Meanwhile, wave after wave of information technologies, such as (statistical) AI, IoT, Digital Twins, Low-Code, No-Code, etc, bring the promise of enabling enterprises to be more intelligent, more efficient, more flexible, and even more agile. Therefore, it is to be expected that future enterprises will be increasingly knowledge-guided model-driven, AI-powered and data-fueled; what we prefer to call AI-native enterprises. The emergence of Knowledge-guided AI-native Adaptive enterprises, however, also raises fundamental design challenges: How to ensure coherent design of such enterprises? How to balance change and stability? How to manage uncertainty? How remain (just enough) compliant to regulations? What about ethics and privacy?

Furthermore, what is the future role of existing disciplines such as Enterprise Modelling, Enterprise Engineering & Architecting, Modelling & Simulation, Process Engineering, Knowledge Engineering, and AI towards the emergence of AI-native enterprises? How do fundamental concepts such as actor-network theory, multi-agent system theory, and control theory fit? Can novel technologies, such as Machine Learning, Adaptive Software, Digital Twins, and Reinforced Learning, further enable the emergence of AI-native enterprises?

The workshop aims to discuss these, and other relevant, issues across the entire gamut ranging over the state of art and practice, limitations and lacunae, possible means to overcome them, case studies illustrating the line of attack, and future work. As such, the goal of this workshop is to bring together leading researchers across different relevant fields, in order to (1) explore the challenges facing the emergence of AI-native enterprises, and (2) exchange and discuss ide-as, concepts, approaches that aim to meet these challenges.

The workshop program comprises of invited talks (45 mins each) by leading researchers from the relevant fields followed by open discussion towards the end. We have received confirmations from the following as invited speakers:

Prof Tony Clark
Aston University Birmingham
UK

Prof Benoit Combemale
University of Rennes 1
France

Prof Erik Proper
Luxembourg Institute of Science and Technology
Luxembourg

Sreedhar Reddy
TCS Research
India

Speakers will be invited to submit their talks to a special issue journal


TARGET AUDIENCE

Academic as well as industrial researchers, students, and industry practitioners having an exposure to software systems and modeling in general.


TOPIC OF INTEREST

The workshop welcomes contributions on the topics mentioned below but is also open to new questions regarding program equivalence. This includes related research areas of relational reasoning like program refinement or the verification of hyperproperties, in particular of secure information flow.

  • Multi-agent systems
  • Language Engineering
  • Enterprise Digital Twins (EDT)
  • Model Driven Engineering of/for EDTs
  • Model Driven Software Development
  • AI for Code Generation
  • Enterprise Modelling
  • Knowledge Engineering
  • Multi-paradigm & Multi-level Modelling
  • Reinforcement Learning
  • Machine Learning
  • Natural Language Processing
  • [Self]Adaptive Software

ORGANIZERS

Vinay Kulkarni

Vinay Kulkarni is Distinguished Chief Scientist at Tata Consultancy Services Research. His research interests include learning-native software systems, multi agent systems, model-driven software engineering, and enterprise modeling. At present, exploring feasibility of future proofing enterprises with modeling, simulation and analytics. The vision is to integrate modeling, learning techniques and control theory to support dynamic adaptation of complex systems of systems using digital twins. This research takes into consideration two hitherto less addressed aspects of partial information and inherent uncertainty. Also investigating programming language, architecture and execution support to realize learning-native software systems. Prior work focused on making software development an engineering endeavour with a-priori guarantees about properties such as correctness, scalability, productivity, maintainability etc. Involved in development of automation tools and method using model-based techniques that are used by industry for past several years. Much of this work has found way into OMG standards, three of which Vinay contributed to in a leadership role. Recently, Vinay got inducted as Fellow of Indian National Academy of Engineering. An alumnus of Indian Institute of Technology Madras, Vinay also serves as Visiting Professor at Middlesex University London UK and Indian Institute of Technology Jodhpur.

Duration
  • Half Day workshop
Organizers