Author name cluster

Julie Porteous

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

23 papers

2 author rows

AAMAS Conference 2025 Conference Paper

Beyond Goal Recognition: A Reinforcement Learning-based Approach to Inferring Agent Behaviour

Sheryl Mantik
Michael Dann
Minyi Li
Huong Ha
Julie Porteous

Goal recognition (GR) involves inferring an agent’s goals based on observed actions. In addition to goals, however, in various cases it may be useful to infer additional agent attributes, such as preferences, beliefs, and ability level, so as to gain deeper insights into the agent’s decision-making process. Recent advances in GR have incorporated Reinforcement Learning (RL), which provides greater practicality and adaptability, especially in stochastic environments. This adaptability creates the opportunity to extend RL-based frameworks beyond goal recognition. In this work, we build upon a recent RL-based GR framework to propose a generalised approach capable of inferring a wider range of agent attributes. By integrating these attributes within the problem formulation, we demonstrate how off-the-shelf RL techniques can be applied to infer them effectively. Our results show that this extended framework accurately distinguishes fine-grained differences in agent attributes across diverse scenarios. Moreover, we show that recognising these additional attributes can in turn improve goal recognition accuracy.

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ICAPS Conference 2024 Conference Paper

The Story So Far on Narrative Planning

Rogelio E. Cardona-Rivera
Arnav Jhala
Julie Porteous
R. Michael Young

Narrative planning is the use of automated planning to construct, communicate, and understand stories, a form of information to which human cognition and enaction is pre-disposed. We review the narrative planning problem in a manner suitable as an introduction to the area, survey different plan-based methodologies and affordances for reasoning about narrative, and discuss open challenges relevant to the broader AI community.

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AAMAS Conference 2023 Conference Paper

Communicating Agent Intentions for Human-Agent Decision Making under Uncertainty

Julie Porteous
Alan Lindsay
Fred Charles

Recent advances in visualisation technologies have opened up new possibilities for human-agent communication. For systems where agents use automated planning, visualisation of agent intentions, i. e. , agent planned actions, can assist human understanding and decision making (e. g. , deciding when human control is required or when it can be delegated to an agent). We are working in an application area, shipbuilding, where branched plans are often essential, due to the typical uncertainty experienced. Our focus is how best to communicate, using visualisation, the key information content of branched plans. It is important that such visualisations communicate the complexity and variety of the possible agent intentions i. e. , executions, captured in a branched plan, whilst also connecting to the practitioner’s understanding of the problem. Thus we utilise an approach to generate the complete branched plan, to be able to provide a full picture of its complexity, and a mechanism to select a subset of diverse traces that characterise the possible agent intentions. We have developed an interface which uses 3D visualisation to communicate details of these characterising execution traces. Using this interface, we conducted a study evaluating the impact of different modes of presentation on user understanding. Our results support our expectation that visualisation of branched plan characterising execution traces increases user understanding of agent intention and plan execution possibilities.

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IJCAI Conference 2022 Conference Paper

Automated Sifting of Stories from Simulated Storyworlds

Wilkins Leong
Julie Porteous
John Thangarajah

Story sifting (or story recognition) allows for the exploration of events, stories, and patterns that emerge from simulated storyworlds. The goal of this work is to reduce the authoring burden for creating sifting queries. In this paper, we use the event traces of simulated storyworlds to create Dynamic Character Networks that track the changing relationship scores between characters in a simulation. These networks allow for the fortunes between any two characters to be plotted against time as a story arc. Similarity scores between story arcs from the simulation and a user’s query arc can be calculated using the Dynamic Time Warping algorithm. Events corresponding to the story arc that best matches the query arc can then be returned to the user, thus providing an intuitive means for users to sift a variety of stories without coding a search query. These components are implemented in our experimental prototype ARC SIFT. The results of a user study support our expectation that ARC SIFT is an intuitive and accurate tool that allows human users to sift stories out from a larger chronicle of events emerging from a simulated story world.

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AAMAS Conference 2022 Conference Paper

Automated Story Sifting Using Story Arcs

Wilkins Leong
Julie Porteous
John Thangarajah

Story sifting (or story recognition) allows for the exploration of events, stories and patterns that emerge from agent-based simulations. The goal of this work is to automate and reduce the authoring burden for writing sifting queries. In this paper, we use the event traces of agent-based simulations to create Dynamic Character Networks that track the changing relationship scores between every agent in a simulation. These networks allow for the fortunes between any two agents to be plotted against time as a story arc. Similarity scores between story arcs from the simulation and a user’s query arc can be calculated using the Dynamic Time Warping technique. Events corresponding to the story arc that best matches the query arc can then be returned to the user, thus providing an intuitive means for users to sift a variety of stories without coding a search query. These components are implemented in our experimental prototype Arc Sift. The results of a user study support our expectation that Arc Sift is an intuitive and accurate tool that allows human users to sift stories out from a larger chronicle of events produced by an agent-based simulation.

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JAAMAS Journal 2021 Journal Article

Automated narrative planning model extension

Julie Porteous
João F. Ferreira
Marc Cavazza

Abstract Interactive Narrative is an emerging application of automated planning, in which a planning domain is used to generate a consistent chain of narrative actions that constitute a plot structure. The task of creating narrative planning domains has been identified as a bottleneck which is hampering further development of the field. This stems from the difficulties faced by humans authoring such planning domains due to the need to provide the range of alternative content, such as actions, which are required to support the important properties of diversity and robustness. Narrative planning domains must be capable of generating diverse sets of narratives to ensure system replayability, and they must also be able to respond robustly in the face of narrative execution failure due to user interaction. In this paper, we introduce a novel approach to the development of narrative planning domains based on the automatic expansion of a baseline planning domain through application of principled operations applied to both operators and predicates. We overview two such operations in this paper. The first of these, anton for anton ymic operators, is based on the generation of contrary operators that can be invoked in the face of action failure, and whose structure is derived from a model of state transitions triggered by the original operator. Since the intention is for additional operators to be incorporated to the baseline, human-authored, domain model, the generated contents should be human-readable. This is achieved by using combined linguistic resources to access antonyms of predicates occurring inside operators, and parsing them from and into hyphenated units. The second operation, part of the same approach, referred to as thype, generates variants of operators by exploring type hierarchies for the main concepts associated with individual operators; the resulting concepts being fully integrated into a new operator’s structure. Our evaluation procedures are directly derived from the target properties of narrative planning domains, which are diversity and robustness, the former being measured through plot diversity and the latter, plot continuation following planned action execution failure. We used published narrative domains as datasets for these evaluations. Results demonstrated strong generative ability, and even more significant plan completion following action failure. Moreover, our evaluation demonstrates the synergic nature of anton and thype when applied simultaneously. Future work will focus on improving the integration of anton and thype operations through better balance between linguistic and conceptual hierarchies.

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AAAI Conference 2021 Conference Paper

Narrative Plan Generation with Self-Supervised Learning

Mihai Polceanu
Julie Porteous
Alan Lindsay
Marc Cavazza

Narrative Generation has attracted significant interest as a novel application of Automated Planning techniques. However, the vast amount of narrative material available opens the way to the use of Deep Learning techniques. In this paper, we explore the feasibility of narrative generation through selfsupervised learning, using sequence embedding techniques or auto-encoders to produce narrative sequences. We use datasets of well-formed plots generated by a narrative planning approach, using pre-existing, published, narrative planning domains, to train generative models. Our experiments demonstrate the ability of generative sequence models to produce narrative plots with similar structure to those obtained with planning techniques, but with significant plot novelty in comparison with the training set. Most importantly, generated plots share structural properties associated with narrative quality measures used in Planning-based methods. As planbased structures account for a higher level of causality and narrative consistency, this suggests that our approach is able to extend a set of narratives with novel sequences that display the same high-level narrative properties. Unlike methods developed to extend sets of textual narratives, ours operates at the level of plot structure. Thus, it has the potential to be used across various media for plots of significant complexity, being initially limited to training and generation operating in the same narrative genre.

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AAAI Conference 2020 Conference Paper

Narrative Planning Model Acquisition from Text Summaries and Descriptions

Thomas Hayton
Julie Porteous
Joao Ferreira
Alan Lindsay

AI Planning has been shown to be a useful approach for the generation of narrative in interactive entertainment systems and games. However, the creation of the underlying narrative domain models is challenging: the well documented AI planning modelling bottleneck is further compounded by the need for authors, who tend to be non-technical, to create content. We seek to support authors in this task by allowing natural language (NL) plot synopses to be used as a starting point from which planning domain models can be automatically acquired. We present a solution which analyses input NL text summaries, and builds structured representations from which a PDDL model is output (fully automated or author in-theloop). We introduce a novel sieve-based approach to pronoun resolution that demonstrates consistently high performance across domains. In the paper we focus on authoring of narrative planning models for use in interactive entertainment systems and games. We show that our approach exhibits comprehensive detection of both actions and objects in the systemextracted domain models, in combination with signiﬁcant improvement in the accuracy of pronoun resolution due to the use of contextual object information. Our results and an expert user assessment show that our approach enables a reduction in authoring effort required to generate baseline narrative domain models from which variants can be built.

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AAMAS Conference 2019 Conference Paper

Protagonist vs Antagonist PROVANT: Narrative Generation as Counter Planning: Socially Interactive Agents Track

Julie Porteous
Alan Lindsay

Our motivation in this work is to develop a narrative generation mechanism for Interactive Storytelling that removes some of the authoring burden that is inherent to plan-based approaches. We focus on the class of narratives that dominate in Hollywood movies, television serial dramas and situation comedies. These narratives revolve around a central Protagonist in pursuit of a goal and who faces a series of obstructions placed in their way by an Antagonist and which they must overcome in order to reach their goal. We cast this problem as a non-cooperative multi-agent planning problem, in other words counter planning. We build on recent techniques in goal recognition and landmark identification to develop a novel plan-based narrative generation mechanism. A key opportunity that goal recognition provides is to reason explicitly with partially observed action sequences, reflecting the reasoning process of the antagonist. Thus the antagonist can only act to obstruct if it is reasonable (to the viewer) that they have guessed the protagonist’s intentions. Starting from the believed goal, the narrative generator can reason about the protagonist’s plan and what must be done to achieve it i. e. , the plan landmarks [8] and use these to automatically identify suitable points of obstruction. In the paper we detail the approach and illustrate it with a worked example. We report the results of an experimental evaluation and user study in a number of representative narrative domains. The experimental results show that we can construct narratives displaying the desired structure without the overhead of authoring narrative structuring information. Results of the user study with system generated narratives confirm that viewers can clearly recognise agent roles and narrative structure.

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AAMAS Conference 2018 Conference Paper

Benchmark Framework for Virtual Students' Behaviours

Jean-Luc Lugrin
Fred Charles
Michael Habel
Jamie Matthews
Henrik Dudaczy
Sebastian Oberd�rfer
Alice Wittmann
Christian Seufert

This paper demonstrates the integration and evaluation of different atmosphere models into Virtual Reality (VR) training for teacher education. We developed three behaviour models to simulate different levels of class discipline. We evaluated their performances using a combination of objective and subjective measurements. Our initial results suggest that the more believable and distinguishable classroom atmospheres are produced by creating more consistent behaviours across virtual students. Our results confirm the importance of similar behaviours to elicit a particular atmosphere.

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AAMAS Conference 2017 Conference Paper

An Interactive Narrative Platform for Story Understanding Experiments

Julie Porteous
Fred Charles
Cameron Smith
Marc Cavazza
Jolien Mouw
Paul van den Broek

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ICAPS Conference 2017 Conference Paper

Framer: Planning Models from Natural Language Action Descriptions

Alan Lindsay
Jonathon Read
João F. Ferreira 0001
Thomas Hayton
Julie Porteous
Peter Gregory

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.

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AAMAS Conference 2017 Conference Paper

Mise-En-Scè ne of Narrative Action in Interactive Storytelling

Jamie Matthews
Fred Charles
Julie Porteous
Alexandra Mendes

In this Interactive Storytelling (IS) work, we provide a framework for automating several aspects of staging the activities of a population of narrative agents and their interactions. Narrative agents can have differing levels of narrative relevance within the situated narrative actions which are visualised within a 3D real-time virtual world. The solution we propose here offers a framework for integrating the use of multiple dynamic regions within the 3D story world defined through a semantic representation that is able to support the staging of narrative actions through the behaviours of the primary and background agents’ involved. This includes both the mechanics of dealing with the narrative discourse level as well as the interaction with the narrative generation layer to account for any dynamic modifications of the 3D story world. We refer to this approach as MISE-en-scène Region support (MISER) and are providing here details of the demonstration application which users will be able to interact with.

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AAMAS Conference 2017 Conference Paper

MISER: Mise-En-Scè ne Region Support for Staging Narrative Actions in Interactive Storytelling

Jamie Matthews
Fred Charles
Julie Porteous
Alexandra Mendes

The recent increase in interest in Interactive Storytelling systems, spurred on by the emergence of affordable virtual reality technology, has brought with it a need to address the way in which narrative content is visualized through the complex staging of multiple narrative agents’ behaviors within virtual story worlds. In this work we address the challenge of automating several aspects of staging the activities of a population of narrative agents and their interactions, where agents can have differing levels of narrative relevance within the situated narrative actions. Our solution defines an approach that integrates the use of multiple dynamic regions within a virtual story world, specified via a semantic representation that is able to support the staging of narrative actions through the behaviors of the primary and background agents’ that are involved. This encompasses both the mechanics of dealing with the narrative discourse level as well as the interaction with the narrative generation layer to account for any dynamic modifications of the virtual story world. We refer to this approach as mise-en-scène region (miser) support. In this paper, we describe our approach and its integration as part of a fully implemented Interactive Storytelling system. We illustrate the work through detailed examples of short narrative instantiations. We present the results of our evaluation which clearly demonstrate the potential of the miser approach, as well as its scalability.

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AAMAS Conference 2017 Conference Paper

Using Virtual Narratives to Explore Children's Story Understanding

Julie Porteous
Fred Charles
Cameron Smith
Marc Cavazza
Jolien Mouw
Paul van den Broek

Interactive Narratives are systems that use automated narrative generation techniques to create multiple story variants which can be shown to an audience, as virtual narratives, using cinematic staging techniques. Previous research in this area has focused on assessment of aspects such as the quality of the automatically generated narratives and their acceptance by the audience. However in our work we deviate from this to explore the use of interactive narratives to support cognitive psychology experiments in story understanding. We hypothesized that the use of virtual narratives would enable narrative comprehension to be studied independently of linguistic phenomena. To assess this we developed a demonstration interactive narrative featuring a virtual environment (Unity3D engine) based on a pre-existing children’s story which allows for the generation of variants of the original story that can be "told" via visualization in the 3D world. In the paper we introduce a narrative generation mechanism that provides control over insertion of cues facilitating story understanding, whilst also ensuring that the plot itself is unaffected. An intuitive user interface allows experimenters to insert and order cues and specific events while the narrative generation techniques ensure these requests are effected in a consistent fashion. We also report the results of a field experiment with children (age 9-10) that demonstrates the potential for the use of virtual narratives in story understanding experiments. Our results demonstrated acceptance of virtual narratives, the usability of the system and the impact of cue insertion on inference and story understanding.

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ECAI Conference 2016 Conference Paper

Plan-Based Narrative Generation with Coordinated Subplots

Julie Porteous
Fred Charles
Marc Cavazza

Despite recent progress in plan-based narrative generation, one major limitation is that systems tend to produce a single plotline whose progression entirely determines the narrative experience. However, for certain narrative genres such as serial dramas and soaps, multiple interleaved subplots are expected by the audience, as this tends to be the norm in real-world, human-authored narratives. Current narrative generation techniques have overlooked this important requirement, something which could improve the perceived quality of generated stories. To this end, we have developed a flexible plan-based approach to multiplot narrative generation, that successfully generates narratives conforming to different subplot profiles, in terms of the number of subplots interleaved and the relative time spent on each presentation. We have identified specific challenges such as: distribution of virtual characters across subplots; length of each subplot presentation; and transitioning between subplots.

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AAAI Conference 2015 Conference Paper

Using Social Relationships to Control Narrative Generation

Julie Porteous
Fred Charles
Marc Cavazza

Narrative generation represents an application domain for AI planning where plan quality is related to properties such as shape of plan trajectory. In our work we have developed a plan-based approach to narrative generation that uses character relationships as a key determinant in controlling plan shape (relationships are key in genres such as serial dramas and soaps). Our approach is implemented in a demonstration Interactive Narrative, called NETWORKING, set in the medical drama genre. The system features a user-friendly mechanism for specifying relationships between virtual characters, via a social network and real-time visualisation of generated narratives on a 3D stage.

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AAMAS Conference 2013 Conference Paper

A Social Network Interface to an Interactive Narrative

Julie Porteous
Fred Charles
Marc Cavazza

Regular viewers of serial dramas tend to construct a model of the social relationships between characters as a main determinant for the narrative events that constitute each episode. We have developed a novel approach to interactive narrative in which this dependency is made explicit and can be used to control the generation of various episodes for a baseline drama. This is implemented in our demonstration system, NetworkING (social Network for Interactive Narrative Generation), which provides an interface that enables users to set the social relationships between virtual characters in order to create an episode of their choosing which they can watch as it is visualised as a 3D animation. The domain for the interactive narrative is a medical drama with a cast of virtual characters, such as doctors, nurses and patients. The use of the social network makes the relationships between characters visible and hence leads to the generation of narratives featuring shenanigans within the context of medical story lines.

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AAMAS Conference 2011 Conference Paper

Controlling Narrative Time in Interactive Storytelling

Julie Porteous
Jonathan Teutenberg
Fred Charles
Marc Cavazza

Narrative time has an important role to play in Interactive Storytelling (IS). The prevailing approach to controlling narrative time has been to use implicit models that allow only limited temporal reasoning about virtual agent behaviour. In contrast, this paper proposes the use of an explicit model of narrative time which provides a control mechanism that enhances narrative generation, orchestration of virtual agents and number of possibilities for the staging of agent actions. This approach can help address a number of problems experienced in IS systems both at the level of execution staging and at the level of narrative generation. Consequently it has a number of advantages: it is more flexible with respect to the staging of virtual agent actions; it reduces the possibility of timing problems in the coordination of virtual agents; and it enables more expressive representation of narrative worlds and narrative generative power. Overall it provides a uniform, consistent, principled and rigorous approach to the problem of time in agent-based storytelling. In the paper we demonstrate how this approach to controlling narrative time can be implemented within an IS system and illustrate this using our fully implemented IS system that features virtual agents inspired by Shakespeare's The Merchant of Venice. The paper presents results of an experimental evaluation with the system that demonstrates the use of this approach to co-ordinate the actions of virtual agents and to increase narrative generative power.

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AAMAS Conference 2011 Conference Paper

Interactive Storytelling with Temporal Planning

Julie Porteous
Jonathan Teutenberg
Fred Charles
Marc Cavazza

Narrative time has an important role to play in Interactive Storytelling (IS) systems. In contrast to prevailing IS approaches which use implicit models of time, in our work we have used an explicit model of narrative time. The goal of the demonstration IS system is to show how this explicit temporal representation and reasoning can help overcome certain problems experienced in IS systems such as the coordination of virtual agents and system inflexibility with respect to the staging of virtual agent actions. The fully implemented system features virtual agents and situations inspired by Shakespeare's play The Merchant of Venice.

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ICAPS Conference 2011 Conference Paper

Visual Programming of Plan Dynamics Using Constraints and Landmarks

Julie Porteous
Jonathan Teutenberg
David Pizzi
Marc Cavazza

In recent years, there has been considerable interest in the use of planning techniques in the area of new media. Many traditional planning notions no longer apply in the context of these applications. In particular, it can be difficult to answer the important question of what constitutes a good plan for the domain, but there is an emerging consensus that plan dynamics play an important role. As a consequence, it is important to support representation of such aspects. Our solution is to introduce a meta-level of representation that is an abstraction of the domain with respect to both time and causality, and to develop a visual representation of this in the form of a narrative arc. This visual representation can then be used in a visual programming approach to the exploration and specification of plan dynamics. In the paper we outline this approach to meta-level representation using constraints along with the visual programming interface we have developed. We illustrate the approach with examples of visual programming in the development of an interactive entertainment system based on Shakespeare's play ``The Merchant of Venice''

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TIST Journal 2010 Journal Article

Applying planning to interactive storytelling

Julie Porteous
Marc Cavazza
Fred Charles

We have seen ten years of the application of AI planning to the problem of narrative generation in Interactive Storytelling (IS). In that time planning has emerged as the dominant technology and has featured in a number of prototype systems. Nevertheless key issues remain, such as how best to control the shape of the narrative that is generated (e.g., by using narrative control knowledge, i.e., knowledge about narrative features that enhance user experience) and also how best to provide support for real-time interactive performance in order to scale up to more realistic sized systems. Recent progress in planning technology has opened up new avenues for IS and we have developed a novel approach to narrative generation that builds on this. Our approach is to specify narrative control knowledge for a given story world using state trajectory constraints and then to treat these state constraints as landmarks and to use them to decompose narrative generation in order to address scalability issues and the goal of real-time performance in larger story domains. This approach to narrative generation is fully implemented in an interactive narrative based on the “Merchant of Venice.” The contribution of the work lies both in our novel use of state constraints to specify narrative control knowledge for interactive storytelling and also our development of an approach to narrative generation that exploits such constraints. In the article we show how the use of state constraints can provide a unified perspective on important problems faced in IS.

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AAMAS Conference 2010 Conference Paper

Narrative Generation through Characters' Point of View

Julie Porteous
Marc Cavazza
Fred Charles

Virtual Actors are at the heart of Interactive Storytellingsystems and in recent years multiple approaches have beendescribed to specify their autonomous behaviour. One wellknown problem is how to achieve a balance between thecharacters' autonomy, defined in terms of their individualroles and motivations, and the global structure of the plot, which tends to emphasise narrative phenomena and the coordination of multiple characters. In this paper we report anew approach to the definition of virtual characters aimed atachieving a balance between character autonomy and globalplot structure. Where previous approaches have tended tofocus on individual actions our objective is to reincorporatehigher-level narrative elements in the behaviour of individual actors and address the relation between character andplot at the level of behaviour representation. To this endwe introduce the notion of a characters' Point of View andshow how it enables a story to be described from the perspective of a number of different characters: it is not merelya presentation effect it is also a different way to tell a story. As an illustration, we have developed an Interactive Narrative based on Shakespeare's Merchant of Venice. The system, which features a novel planning approach to story generation, can generate very different stories depending on thePoint of View adopted and support dynamic modification ofthe story world which results in different story consequences. In the paper, we illustrate this approach using example narratives generated using our fully implemented prototype.

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