Framer: Planning Models from Natural Language Action Descriptions

In this paper, we describe an approach for learning planning domain models directly from natural language (NL) descriptions of activity sequences. The modelling problem has been identified as a bottleneck for the widespread exploitation of various technologies in Artificial Intelligence, including automated planners. There have been great advances in modelling assisting and model generation tools, including a wide range of domain model acquisition tools. However, for modelling tools, there is the underlying assumption that the user can formulate the problem using some formal language. And even in the case of the domain model acquisition tools, there is still a requirement to specify input plans in an easily machine readable format. Providing this type of input is impractical for many potential users. This motivates us to generate planning domain models directly from NL descriptions, as this would provide an important step in extending the widespread adoption of planning techniques. We start from NL descriptions of actions and use NL analysis to construct structured representations, from which we construct formal representations of the action sequences. The generated action sequences provide the necessary structured input for inducing a PDDL domain, using domain model acquisition technology. In order to capture a concise planning model, we use an estimate of functional similarity, so sentences that describe similar behaviours are represented by the same planning operator. We validate our approach with a user study, where participants are tasked with describing the activities occurring in several videos. Then our system is used to learn planning domain models using the participants’ NL input. We demonstrate that our approach is effective at learning models on these tasks. Introduction Modelling problems appropriately for use by a computer program has been identified as a key bottleneck in the exploitation of various AI technologies. In Automated Planning, this has inspired a growing body of work that aims to support the modelling process including domain acquisition tools, which learn a formal domain model of a system from some form of input data. There is interest in applying domain model acquisition across a range of research and application areas. For example within the business process community (Hoffmann, Weber, and Kraft 2012) and Copyright c © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. space applications (Frank et al. 2011). An extended version of the LOCM domain model acquisition system (Cresswell, McCluskey, and West 2009) has also been used to help in the development of a puzzle game (Ersen and Sariel 2015) based on spatio-temporal reasoning. Web Service Composition is another area in which domain model acquisition techniques have been used (Walsh and Littman 2008). These tools vary in the specifics of the input language, such as example action sequences (Cresswell, McCluskey, and West 2009; Cresswell and Gregory 2011), or action sequences and a partial domain model (McCluskey et al. 2009; Richardson 2008); the query system by which they acquire the input data, which is typically static training sets, although there are examples working with an interactive querying system (Walsh and Littman 2008; Mehta, Tadepalli, and Fern 2011); and the target model language, including STRIPS (Cresswell, McCluskey, and West 2009; Cresswell and Gregory 2011), probabilistic (Mourão, Petrick, and Steedman 2010), and numeric (Gregory and Lindsay 2016; Hayton et al. 2016). However, in each case the user is left the responsibility of defining a formal representation for the solution. Defining these logical formalisms and applying them consistently requires time and experience in both the target domain and in the representation language, which many potential users will not have. It is therefore important to consider alternative input languages, such as Natural Language (Goldwasser and Roth 2011). Natural Language (NL) input is the most natural way for humans to interact and it is no surprise that there is much interest in using NL as input for computer systems. In day-to-day life, Siri and its competitors are controlled by simple spoken word input, but can activate complex procedures on our phones. In the RoboCup@Home competitions robots are controlled by task descriptions and are automatically translated into a series of simple actions that can be performed on the robot. And NL lessons have been used to learn partial representations of the world dynamics for game-like environments (Goldwasser and Roth 2011). A key aspect of these systems is an underlying language, which the NL input is mapped onto. For example, in the case of RoboCup@Home, an input of ‘go to the living room’ might be mapped onto quite a different representation, using the action name ‘move’ and requiring a set of parameters that break the movement into smaller