Relational approach to knowledge engineering for POMDP-based assistance systems as a translation of a psychological model

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• 作者：Marek Grze艣 ; Jesse Hoey ; Shehroz S. Khan ; Alex Mihailidis ; Stephen Czarnuch ; Dan Jackson ; Andrew Monk
• 关键词：Dynamic Bayesian networks ; Knowledge engineering ; Probabilistic planning ; POMDPs ; Reinforcement learning ; Assistance systems
• 刊名：International Journal of Approximate Reasoning
• 出版年：January, 2014
• 年：2014
• 卷：55
• 期：1_part_P1
• 页码：36-58
• 全文大小：2622 K

文摘

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鈥檚 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. The database encodes the relational skeleton of the PRM, and includes the goals, action preconditions, environment states, cognitive model, client and system actions (i.e., the outcome of the SNAP analysis), as well as relevant sensor models. The database is easy to approach for someone who is not an expert in POMDPs, allowing them to fill in the necessary details of a task using a simple and intuitive procedure. The database, when filled, implicitly defines a ground instance of the relational skeleton, which we extract using an automated procedure, thus generating a POMDP model of the assistance task. A strength of the database is that it allows constraints to be specified, such that we can verify the POMDP model is, indeed, valid for the task given the analysis. We demonstrate the method by eliciting three assistance tasks from non-experts: handwashing, and toothbrushing for elderly persons with dementia, and on a factory assembly task for persons with a cognitive disability. 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.