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Towards Automated AI Planning in Enterprise: Opportunities and Challenges.

Maja Vukovic (IBM)
4:55pm5:35pm Thursday, April 18, 2019
Machine Learning, Models and Methods
Location: Grand Ballroom West
Secondary topics:  AI in the Enterprise, Models and Methods

Who is this presentation for?

Solution Architects, Technical Executives, Business managers planning to implement AI planning



Prerequisite knowledge

Experience in or using different AI techniques in developing automation solutions

What you'll learn

• Understand basics of AI planning • Explore opportunities for AI planning and innovations in this space • Why using AI planning can improve automation • Barriers to adoption of AI planning (e.g. world model creation) • How to structure teams to successfully implement AI planning solutions


Existing AI driven automation solutions in enterprises employ ML, NLP and chatbots. There is an opportunity for AI Planning to be applied, which offers reasoning about action trajectories to help build automation blueprints. AI Planning is a problem-solving technique, where knowledge about available actions and their consequences is used to identify a sequence of actions, which, when applied in a given initial state, satisfy a desired goal 1. There are three main inputs to a planner: initial state, goal condition and domain description. AI Planning has success stories in a number of domains ranging from space applications, logistics and transportation, manufacturing, robotics, scheduling, e-learning and service composition 2.

The common problem where AI Planning is applicable is environments where there are many actions to choose from and many ways to achieve a goal by taking a series of those actions. These actions are fine grained and can be taken in many different situations or contexts through-out the process, and it is not clear which action to take in each situation. Following on, one needs an understanding of how these actions may take you closer to the goal, and similarly situations where you are faced with complex goals which would need to be broken down into multiple ones.

In this talk we discuss experiences in building an automation solution that employs AI planning for use in migration of legacy applications to Cloud. Complexity of migration tasks makes this transformation long and challenging, as the environmental conditions continuously change (e.g. availability of landing pad, application release cycles, etc.). Often delivery project managers continuously and manually replan the migration project activities, which is tedious and time consuming when considering the large scale of transformation projects.

The talk presents how AI planner can expedite and simplify the migration planning process by defining the clients’ constraints and resources in a simplified format that abstracts the user’s need to hardcode domains and problems. This capability is exposed as a service and evaluated for migration plans for multiple clients with varying constraints in the span of a few minutes, thereby enabling migration project manager and migration architects to reason about potential migration plans, and automatically re-plan as needed 3. We discuss challenges in adoption of AI planning across the enterprise from implementation and deployment perspectives.

1. Stuart J. Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Upper Saddle River, NJ, 1995.
2. Maja Vukovic: Context aware service composition. PhD Thesis. University of Cambridge, UK 2007
3. Malik Jackson, Jinho Hwang, John Rofrano, Maja Vukovic. BluePlan: A Service for Automated Migration Plan Construction using AI Planning. ICSOC 2018.

Photo of Maja Vukovic

Maja Vukovic


Maja Vukovic is a Research Manager and a Research Staff Member at IBM T.J. Watson Research Center. Maja’s research expertise is in IT service innovation, AI planning, crowdsourcing technologies, API ecosystems innovation and social media applications for disaster management. Maja leads Cognitive Service Management team, with focus on AI driven insights and automation in hybrid cloud systems.

Maja has received numerous IBM Outstanding Technical Achievement Awards and IBM Research awards for her technical leadership. Maja is an IBM Master Inventor, with over 160 patents filed and 50 granted. Maja is an author of over 90 papers in top international conferences and journals. Maja is a co-founder of a number of workshops: Enterprise Crowdsourcing, Ubiquitous Crowdsourcing and Social Web for Disaster Management, collocated with leading international conferences.

Maja received her PhD from University of Cambridge, UK, for her work on context aware service composition using AI planning. Maja received her MSc from International University in Germany, and her BSc from University of Auckland, New Zealand. Prior to IBM, Maja was a Research Scientist at MercedesBenz Research and Technology Center in Palo Alto, working in the field of telematic services.

Maja is a Member of IBM Academy of Technology.

Maja is a Senior Member of Institute of Electrical and Electronics Engineers (IEEE).

Maja is a IEEE TCSVC Award Winner: Women in Services Computing 2018.

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