Relational Approach to Knowledge Engineering for POMDP-based Assistance Systems as a Translation of a Psychological Model

TL;DR

This paper presents an automated method to translate a psychological model into POMDPs for assistive systems, reducing manual effort and enabling scalable, context-aware assistance for persons with cognitive disabilities.

Contribution

It formalizes and automates the translation from a relational model to POMDPs using a probabilistic relational model and database, streamlining the development of assistive systems.

Findings

Automated translation process reduces manual effort in POMDP creation.

Validated POMDP models through simulations and real-life experiments.

Demonstrated elicitation of assistance tasks from non-experts.

Abstract

Assistive systems for persons with cognitive disabilities (e.g. dementia) are difficult to build due to the wide range of different approaches people can take to accomplishing the same task, and the significant uncertainties that arise from both the unpredictability of client's behaviours and from noise in sensor readings. Partially observable Markov decision process (POMDP) models have been used successfully as the reasoning engine behind such assistive systems for small multi-step tasks such as hand washing. POMDP models are a powerful, yet flexible framework for modelling assistance that can deal with uncertainty and utility. Unfortunately, POMDPs usually require a very labour intensive, manual procedure for their definition and construction. Our previous work has described a knowledge driven method for automatically generating POMDP activity recognition and context sensitive prompting systems for complex tasks. We call the resulting POMDP a SNAP (SyNdetic Assistance Process). The spreadsheet-like result of the analysis does not correspond to the POMDP model directly and the translation to a formal POMDP representation is required. To date, this translation had to be performed manually by a trained POMDP expert. In this paper, we formalise and automate this translation process using a probabilistic relational model (PRM) encoded in a relational database. We demonstrate the method by eliciting three assistance tasks from non-experts. We validate the resulting POMDP models using case-based simulations to show that they are reasonable for the domains. We also show a complete case study of a designer specifying one database, including an evaluation in a real-life experiment with a human actor.