Interactive planning-based hypothesis generation with LTS++

Abstract

We present LTS++, an interactive development environment for planning-based hypothesis generation motivated by applications that require multiple hypotheses to be generated in order to reason about the observations. Our system uses expert knowledge and AI planning to reason about possibly incomplete, noisy, or inconsistent observations derived from data by a set of analytics, and generates plausible and consistent hypotheses about the state of the world. Planning-based reasoning is enabled by knowledge models obtained from domain experts that describe entities in the world, their states, and relationship to observations. To address the knowledge engineering challenge, we have developed a language, also called LTS++ that allows the domain expert to specify the state transition model and encoding of the observations without any knowledge of AI planning or existing planning languages (i.e., PDDL). LTS++ integrated development environment facilitates model testing and debugging, generating, and visualizing multiple hypotheses for user-provided observations, and supports model deployment for online observation processing, publishing generated hypotheses for analysis by experts or other systems. To compute hypotheses we use an efficient planner that finds a set of high-quality plans. We experimentally evaluate our planning algorithm and conduct empirical evaluation to demonstrate the feasibility of our approach and the benefits of using planning-based reasoning. In this chapter we focus on describing the modeling and the knowledge engineering challenges of our system.