Incorporating Behavioral Recommendations Mined from Event Logs into AI Planning
Gyunam Park, Majid Rafiei, Hayyan Helal, Gerhard Lakemeyer, Wil M. P. Van Der Aalst
January, 2024Abstract
AI planning plays a crucial role in the design and optimization of business processes, providing optimal plans, i.e., sequence of activities, based on manually crafted or formally documented rules. When these plans are executed in business processes, the supporting information systems record a wealth of event data. Analyzing such event data facilitates understanding implicit patterns and recommendations that have the potential to refine planning strategies significantly. In this paper, we introduce a systematic approach to mining these recommendations from event data and integrating them into AI planning, thus creating plans that are informed by both the regulatory hard rules and the flexibility of soft recommendations.
Publication
36th International Conference on Advanced Information Systems Engineering Forum, CAiSE Forum2024, Limassol, Cyprus, 03-07 June, 2024, Proceedings
Practical problem
Study advisors, logistics planners and clinicians rely on automated planners to lay out sequences of steps, for example which courses a student should take in which order. Those planners work from hard rules written down in regulations or process models, such as a mandatory prerequisite, and know nothing about what has historically worked well, such as the observation that students who take Algorithms first do better in Advanced Data Structures. That experience sits unused in the event data that information systems record every time a plan is executed. Specifying such preferences by hand is slow and incomplete, so plans stay valid but miss the opportunity to steer people toward behavior that has proven beneficial.
Research questions
- How can behavioral recommendations (soft rules) be mined from event logs that record executed plans, and how can their relevance be tested statistically?
- How can such recommendations be combined with regulatory hard rules inside AI planning so that generated plans are both valid and preferred?
Contributions
- A two-phase approach that mines behavioral recommendations from event data and feeds them into AI planning as soft constraints alongside hard regulatory rules.
- A mining procedure that instantiates Declare temporal pattern templates (precedence, response, exclusive choice), checks each case with Linear Temporal Logic (LTL), and keeps only patterns whose satisfied and violated cases differ significantly on an outcome score.
- Formalizes recommendation-based planning problems with a weight function that sums the weights of recommended temporal patterns holding in a plan, defining optimal plans as valid plans of maximal weight.
- Shows that recommendation-based planning with each action occurring at most once can be solved as an integer linear programming optimization over plan membership and ordering relations.
- A running example from curriculum design in which course prerequisites act as hard rules and mined course-ordering patterns act as soft rules.
Key findings
- This is a short vision paper without experiments; it argues that event data from executed plans contains statistically testable behavioral patterns that current planners ignore.
- It shows by construction how a Declare pattern such as Precedence(Algorithms, Advanced Data Structures) becomes a weighted preference that raises the rank of plans respecting it while prerequisites remain mandatory.
- It establishes that, because any valid plan with non-repeating actions has linear length, the planning problem is in NP and can be encoded as an integer linear program maximizing the recommendation weight.