Knowledge Engineering for Planning-Based Hypothesis Generation

TL;DR

This paper introduces a planning-based approach for hypothesis generation in critical applications like healthcare and cybersecurity, using a new modeling language and tool to handle uncertainty and incomplete data.

Contribution

It presents LTS++, a new language and a 9-step process for knowledge engineering, enabling effective hypothesis generation with AI planning under uncertainty.

Findings

Developed LTS++, a modeling language for hypothesis generation.

Created a web-based tool for LTS++ model specification.

Demonstrated the approach in healthcare and cybersecurity scenarios.

Abstract

In this paper, we address the knowledge engineering problems for hypothesis generation motivated by applications that require timely exploration of hypotheses under unreliable observations. We looked at two applications: malware detection and intensive care delivery. In intensive care, the goal is to generate plausible hypotheses about the condition of the patient from clinical observations and further refine these hypotheses to create a recovery plan for the patient. Similarly, preventing malware spread within a corporate network involves generating hypotheses from network traffic data and selecting preventive actions. To this end, building on the already established characterization and use of AI planning for similar problems, we propose use of planning for the hypothesis generation problem. However, to deal with uncertainty, incomplete model description and unreliable observations, we need to use a planner capable of generating multiple high-quality plans. To capture the model description we propose a language called LTS++ and a web-based tool that enables the specification of the LTS++ model and a set of observations. We also proposed a 9-step process that helps provide guidance to the domain expert in specifying the LTS++ model. The hypotheses are then generated by running a planner on the translated LTS++ model and the provided trace. The hypotheses can be visualized and shown to the analyst or can be further investigated automatically.