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KEPS 2017 Proceedings of the Workshop on Knowledge Engineering for Planning and Scheduling 27th International Conference on Automated Planning and Scheduling June 19-23, 2017, Pittsburgh, USA KEPS 2017 Proceedings of the Workshop on Knowledge Engineering for Planning and Scheduling Edited by: Lukas Chrpa, Mauro Vallati and Tiago Vaquero Organization Lukas Chrpa, Czech Technical University & Charles University in Prague, Czech Republic Mauro Vallati, University of Huddersfield, UK Tiago Vaquero, MIT, USA Program Committee Dimitris Vrakas, Aristotle University of Thessaloniki, Greece Shirin Sohrabi, IBM Research, USA Patricia Riddle, University of Auckland, New Zealand Julie Porteous, Teesside University, UK Ron Petrick, Heriot-Watt University, UK Simon Parkinson, University of Huddersfield, UK Andrea Orlandini, National Research Council of Italy (ISTC-CNR), Italy Lee Mccluskey, University of Huddersfield, UK Simone Fratini, European Space Agency - ESA/ESOC, Germany Jeremy Frank, NASA, USA Susana Fernandez, Universidad Carlos III de Madrid, Spain Amedeo Cesta, National Research Council of Italy (CNR), Italy Luis Castillo, University of Granada, Spain Adi Botea, IBM Research , Ireland Roman Barták, Charles University, Czech Republic Contents StoryFramer: From Input Stories to Output Planning Models...................................................1 Thomas Hayton, Julie Porteous, Joao Ferreira, Alan Lindsay and Jonathan Read A PDDL Representation for Contradance Composition...........................................................10 Richard Freedman and Shlomo Zilberstein Integrating Modeling and Knowledge Representation for Combined Task, Resource and Path Planning in Robotics..............................................................................................................18 Simone Fratini, Tiago Nogueira and Nicola Policella Classical Planning in Latent Space: From Unlabeled Images to PDDL (and back).....................27 Masataro Asai and Alex Fukunaga Planning-based Scenario Generation for Enterprise Risk Management..................................36 Shirin Sohrabi, Anton Riabov and Octavian Udrea Attribute Grammars with Set Attributes and Global Constraints as a Unifying Framework for Planning Domain Models.......................................................................................................45 Roman Barták and Adrien Maillard Method Composition through Operator Pattern Identification...............................................54 Maurício Cecílio Magnaguagno and Felipe Meneguzzi Extracting Incomplete Planning Action Models from Unstructured Social Media Data to Support Decision Making....................................................................................................................62 Lydia Manikonda, Shirin Sohrabi, Kartik Talamadupula, Biplav Srivastava and Subbarao Kambhampati Domain Model Acquisition with Missing Information and Noisy Data....................................69 Peter Gregory, Alan Lindsay and Julie Porteous StoryFramer: From Input Stories to Output Planning Models Thomas Hayton1, Julie Porteous1, Joao˜ F. Ferreira1,3, Alan Lindsay1, Jonathon Read2 1Digital Futures Institute, School of Computing, Teesside University, UK. 2Ocado Technology, Hatfield, UK. 3HASLab/INESC TEC, Universidade do Minho, 4704-553 Braga, Portugal. [email protected] | [email protected] Abstract such IS systems. To date the authoring of narrative plan- ning models has been handled manually, a common strategy We are interested in the problem of creating narrative plan- ning models for use in Interactive Multimedia Storytelling being to build up the model via systematic consideration of Systems. Modelling of planning domains has been identified alternatives around a baseline plot (Porteous, Cavazza, and as a major bottleneck in the wider field of planning technolo- Charles 2010b) and many prototype IS systems have sought gies and this is particularly so for narrative applications where inspiration from existing literary or filmic work. Exam- authors are likely to be non-technical. On the other hand there ples include the Fac¸ade interactive drama which was based are many large corpora of stories and plot synopses, in natural on “Who’s Afraid of Virginia Woolf?” (Mateas and Stern language, which could be mined to extract content that could 2005), The Merchant of Venice (Porteous, Cavazza, and be used to build narrative domain models. Charles 2010a), Madame Bovary (Cavazza et al. 2009) and In this paper we describe an approach to learning narrative the tale of Aladdin (Riedl and Young 2010). planning domain models from input natural language plot However this manual creation is extremely challenging. synopses. Our approach, called StoryFramer, takes natural In this paper the problem we tackle is automation of narra- language input and uses NLP techniques to construct struc- tive domain model creation. Our starting point is to look at tured representations from which we build up domain model content. The system also prompts the user for input to disam- natural language plot outlines as content from which to au- biguate content and select from candidate actions and pred- tomatically induce planning models. We are developing a icates. We fully describe the approach and illustrate it with solution which takes natural language input (i.e. stories) and an end-to-end worked example. We evaluate the performance uses NLP techniques to construct structured representations of StoryFramer with NL input for narrative domains which of the text and keeps the user in the loop to guide refinement demonstrate the potential of the approach for learning com- of the planning model. This approach represents an exten- plete domain models. sion to the Framer system of (Lindsay et al. 2017) to appli- cation with narrative domain models. We have implemented Introduction the approach in a prototype system called StoryFramer. Interactive Multimedia Storytelling (IS) systems allow users In the paper we give an overview of the technical aspects to interact and influence, in real-time, the evolution of a of the approach and illustrate it with an end-to-end worked narrative as it is presented to them. This presentation can example using the tale of Aladdin taken from (Riedl and be via a range of different output media such as 2D or 3D Young 2010). We present the results of an evaluation with a animation (Mateas and Stern 2005; Porteous, Charles, and two domain models generated by StoryFramer and consider Cavazza 2013), filmic content (Piacenza et al. 2011) and text the potential of the approach. (Cardona-Rivera and Li 2016). In addition, a range of dif- ferent interaction mechanisms have been used such as emo- Related Work tional speech input (Cavazza et al. 2009), gaze (Bee et al. Some recent work in the area of automated domain model 2010), and physiologicali measures (Gilroy et al. 2012). creation for planning has attempted to learn planner action AI planning has been widely used for narrative generation models from natural language (NL) input. in IS as it provides: a natural “fit” with story plot lines rep- Much of this work has attempted to map from NL input resented as narrative plans; ensures causality which is im- onto existing formal representations. For example, in re- portant for the generation of meaningful and comprehen- lation to RoboCup@Home, Kollar et al. (2013) present a sible narratives; and provides considerable flexibility and probabilistic approach to learning the referring expressions potential generative power. Consequently plan-based ap- for robot primitives and physical locations in a region. Also proaches have featured in many systems (e.g. as reported Mokhtari, Lopes, and Pinho (2016) present an approach to by (Aylett, Dias, and Paiva 2006; Riedl and Young 2010; learning action schemata for high-level robot control. Porteous, Charles, and Cavazza 2013)). In (Goldwasser and Roth 2011) the authors present an In this work we are interested in the problem of author- alternative approach to learning the dynamics of the world ing the narrative planning domain models that are used in where the NL input provides a direct lesson about part of the 1 NL Input Domain Model S1: “ Aladdin takes the magic lamp from the dead body of the dragon ” Domain Problem (define (domain aladdin) (define (problem aladdinProb) (:requirements :typing :equality (:domain aladdin) S2: “ Aladdin travels from the castle to the mountains ” :negative-preconditions) (:objects (:types character object location) Jasmine – character (:constants jasmine – character S3: “ Jasmine is very beautiful ” Jafar – character jafar – character Magic-Lamp – object magic-lamp – object S4: “ The genie is in the magic lamp ” Dragon – character …) Genie – character Aladdin – character (:predicates (at ?x ?y) (has ?x ?y) (woman ?x) a Castle – location (king ?x) (can-travels ?x) … ) Mountains – location ) (:action travels Template Representations (:init (woman Jasmine) User Input :parameters (?c1 – character ?l1 – location (king Jafar) ?l2 – location) (has Dragon Magic-Lamp) Actions :preconditions (and (can-travels ?c1) Removing Duplicates Object Typing (at Aladdin Castle) (not (= ?l1 ?l2)) (at Dragon Mountain) S1 S2 (at ?c1 ?l1) action: takes { Jasmine – character (can-travels Aladdin) action: travels { :effect (and (has-travels ?c1) Jasmine Genie Jafar – character … subject: aladdin (at ?c1 ?l2) subject: aladdin Jafar Aladdin Magic-Lamp – object ) object: magic lamp Magic-Genie Castle Dragon – character (not (at ?c1 ?l1)) ) from: castle Magic-Lamp Mountains Genie – character (:goal (and (dead Genie) (has-wed Jasmine) …
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