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John Thangarajah

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55 papers
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55

AAMAS Conference 2025 Conference Paper

A Scoresheet for Explainable AI

  • Michael Winikoff
  • John Thangarajah
  • Sebastian Rodriguez

Explainability is important for the transparency of autonomous and intelligent systems and for helping to support the development of appropriate levels of trust. There has been considerable work on developing approaches for explaining systems and there are standards that specify requirements for transparency. However, there is a gap: the standards are too high-level and do not adequately specify requirements for explainability. This paper develops a scoresheet that can be used to specify explainability requirements or to assess the explainability aspects provided for particular applications. The scoresheet is developed by considering the requirements of a range of stakeholders and is applicable to Multiagent Systems as well as other AI technologies. We also provide guidance for how to use the scoresheet and illustrate its generality and usefulness by applying it to a range of applications.

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IJCAI Conference 2025 Conference Paper

Direct Estimation of Attenuation Information from Sinograms for Positron Emission Tomography Reconstruction

  • Prabath Hetti Mudiyanselage
  • Ruwan Tennakoon
  • John Thangarajah
  • Robert Ware
  • Jason Callahan

Positron Emission Tomography (PET) is a powerful imaging modality for assessing biochemical processes within the body. However, accurate image reconstruction is challenged by photon attenuation, particularly in dense structures such as bones, leading to quantification errors and reduced diagnostic confidence. Computed Tomography (CT) based attenuation correction is the standard approach but introduces additional radiation exposure, longer imaging times, and patient inconvenience, as well as potential registration errors, motion artifacts, and energy scaling inaccuracies. In this study, we propose a 3D U-Net based deep learning framework that directly estimates attenuation information from PET sinograms, eliminating the need for additional imaging modalities. Our approach integrates PET physics and employs custom skip connections to enhance cross-domain learning. We evaluate our model on a simulated brain dataset derived from real patient templates, achieving a Dice coefficient of 0. 650 and an accuracy of 0. 486 for bone structures. The clinical applicability of our method is further assessed by reconstructing PET images with the generated attenuation maps, yielding an MSE of 0. 007 and an SSIM of 0. 956, demonstrating strong structural consistency with CT-based attenuation correction. These results highlight the feasibility of performing PET image attenuation correction using PET sinograms alone, offering a promising alternative that reduces imaging time, radiation exposure, and patient burden while enabling faster and more efficient PET reconstruction.

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AAMAS Conference 2025 Conference Paper

Requirements-based Explainability for Multi Agent Systems

  • Sebastian Rodriguez
  • John Thangarajah
  • Michael Winikoff

Explainability is essential for building trust in intelligent and autonomous systems. However, existing techniques for explainability focus on the behaviour of the system, but do not go back to the system’s requirements. We provide fully traceable explanations that link back to requirements, expressed as User and System stories, by extending previous work on explainable agents (XAg) that uses an agent design pattern. Our implementation leverages industry-grade mainstream monitoring tools.

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AAMAS Conference 2024 Conference Paper

Design Patterns for Explainable Agents (XAg)

  • Sebastian Rodriguez
  • John Thangarajah
  • Andrew Davey

The ability to explain the behaviour of the AI systems is a key aspect of building trust, especially for autonomous agent systems - how does one trust an agent whose behaviour can not be explained? In this work, we advocate the use of design patterns for developing explainable-by-design agents (XAg), to ensure explainability is an integral feature of agent systems rather than an “add-on” feature. We present TriQPAN (Trigger, Query, Process, Action and Notify), a design pattern for XAg. TriQPAN can be used to explain behaviours of any agent architecture and we show how this can be done to explain decisions such as why the agent chose to pursue a particular goal, why or why didn’t the agent choose a particular plan to achieve a goal, and so on. We term these queries as direct queries. Our framework also supports temporal correlation queries such as asking a search and rescue drone, “which locations did you visit and why? ”. We implemented TriQPAN in the SARL agent language, built-in to the goal reasoning engine, affording developers XAg with minimal overhead. The implementation will be made available for public use. We describe that implementation and apply it to two case studies illustrating the explanations produced, in practice.

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AAMAS Conference 2024 Conference Paper

Explainable Agents (XAg) by Design

  • Sebastian Rodriguez
  • John Thangarajah

The likes of ChatGPT has propelled the use of AI techniques beyond our community’s expectations. Along with this, the fear of AI has also risen, in particular around the ability, or lack thereof, of the AI system to explain its behaviours. Explainability is a key element of building trust and an important issue for our community. In this paper we advocate for agents that are explainable-by-design, that is, explainability is built into the development of agents rather than an afterthought. We propose key features of an explainable agent (XAg) system and propose a general framework that enables explainability. We advocate the use of design patterns to develop XAgs and propose a general design pattern that can be used for any agent architecture. We instantiate our framework for goal-based agents and implement the framework for the SARL agent programming language coupled with a state-of-the-art event management system. We make a call to the developers of other agent programming languages (APLs) in our community to follow suit by instantiating the general framework we propose into their APL, perhaps even enhancing the framework we present. We also propose an open repository of design patterns and examples for agent systems. If nothing else, we hope this paper will inspire further work on XAg from the design perspective as it is critical that multi agent systems are explainable by design!

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AAMAS Conference 2023 Conference Paper

A Behaviour-Driven Approach for Testing Requirements via User and System Stories in Agent Systems

  • Sebastian Rodriguez
  • John Thangarajah
  • Michael Winikoff

Testing is a critical part of the software development cycle. This is even more important for autonomous systems, which can be challenging to test. In mainstream software engineering, Behaviour- Driven Development (BDD) is an Agile software development practice that is well accepted and widely used. It involves defining test cases for the expected system behaviour prior to developing the associated functionality. In this work, we present a BDD approach to testing the behavioural requirements of an agent system specified via User and System Stories (USS). USS is also based on established Agile processes and is shown to be intuitive and readily mapped to agent concepts. More specifically we extend USS so that they can be used for testing, and develop a behaviour-driven testing framework based on USS. We show how test cases can be developed, and how to evaluate the test cases by using a state-of-the-art mutation testing system, PITest, which we have integrated into our test framework. A key feature of our work is that we leverage a range of state-of-the-art development tools, inheriting the rich set of features they provide.

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AAMAS Conference 2023 Conference Paper

Feedback-Guided Intention Scheduling for BDI Agents

  • Michael Dann
  • John Thangarajah
  • Minyi Li

Intelligent agents, like those based on the popular BDI agent paradigm, typically pursue multiple goals in parallel. An intention scheduler is required to reason about the possible interactions between the agent’s intentions to maximize some utility. An important consideration when scheduling intentions is the user’s preferences over the goals and the ways in which the goals are achieved. These preferences are generally unknown in advance, time-consuming to elicit, hard to model, and difficult to incorporate into an intention scheduler. In this paper, we present a Monte Carlo Tree Search based intention scheduler (pref-MCTS) that is able to learn the user’s preferences over intention schedules via low-burden comparativetype queries. It incorporates the learned preferences in guiding the search, leading to execution policies that are optimized towards the user’s preferences and expectations. We evaluate our approach using an artificial oracle that shows that pref-MCTS improves over state-of-the-art baselines, even when provided with a limited number of preference queries and noisy labels. We also conducted a user study and showed that pref-MCTS is able to learn user preferences and generate schedules that are preferred by the users in real-time.

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IJCAI Conference 2023 Conference Paper

Multi-Agent Intention Recognition and Progression

  • Michael Dann
  • Yuan Yao
  • Natasha Alechina
  • Brian Logan
  • Felipe Meneguzzi
  • John Thangarajah

For an agent in a multi-agent environment, it is often beneficial to be able to predict what other agents will do next when deciding how to act. Previous work in multi-agent intention scheduling assumes a priori knowledge of the current goals of other agents. In this paper, we present a new approach to multi-agent intention scheduling in which an agent uses online goal recognition to identify the goals currently being pursued by other agents while acting in pursuit of its own goals. We show how online goal recognition can be incorporated into an MCTS-based intention scheduler, and evaluate our approach in a range of scenarios. The results demonstrate that our approach can rapidly recognise the goals of other agents even when they are pursuing multiple goals concurrently, and has similar performance to agents which know the goals of other agents a priori.

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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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IJCAI Conference 2022 Conference Paper

Multi-Agent Intention Progression with Reward Machines

  • Michael Dann
  • Yuan Yao
  • Natasha Alechina
  • Brian Logan
  • John Thangarajah

Recent work in multi-agent intention scheduling has shown that enabling agents to predict the actions of other agents when choosing their own actions can be beneficial. However existing approaches to 'intention-aware' scheduling assume that the programs of other agents are known, or are "similar" to that of the agent making the prediction. While this assumption is reasonable in some circumstances, it is less plausible when the agents are not co-designed. In this paper, we present a new approach to multi-agent intention scheduling in which agents predict the actions of other agents based on a high-level specification of the tasks performed by an agent in the form of a reward machine (RM) rather than on its (assumed) program. We show how a reward machine can be used to generate tree and rollout policies for an MCTS-based scheduler. We evaluate our approach in a range of multi-agent environments, and show that RM-based scheduling out-performs previous intention-aware scheduling approaches in settings where agents are not co-designed

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AAMAS Conference 2022 Conference Paper

Testing Requirements via User and System Stories in Agent Systems

  • Sebastian Rodriguez
  • John Thangarajah
  • Michael Winikoff
  • Dhirendra Singh

Agile software development is a popular and widely adopted practice due to its flexible and iterative nature that facilitates rapid prototyping. Recent work presented an agile approach to capturing requirements in agent systems via user and system stories. User and system stories present the requirements from the user and system perspective, respectively. Each story contains a set of acceptance criteria, which are a set of statements that identify the conditions under which the system behaviour can be accepted by the users or stakeholders. In this paper, we present a novel approach to testing the requirements that are specified via User and System stories in an agent system. We do this by developing a systematic approach to validating the execution traces output by the system against the specified acceptance criteria for each story. The approach identifies acceptance criteria that are met successfully in execution and those that fail. We present a fault model that categorizes the failures providing insight to the developers to address the failed cases. We classify three kinds of faults for a given acceptance criterion: (a) the trigger condition is never met; (b) when the trigger occurs the preconditions are not met; or (c) the trigger and preconditions are met but the resulting actions are not as expected. The motivating application of our work, which is also the test-bed for evaluation, is an agent-based simulation application for modelling the behaviours of civilians in a bushfire emergency scenario that is used in practice. We show our approach is able to successfully test and uncover requirements that were not met in this application.

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ICLR Conference 2021 Conference Paper

Adapting to Reward Progressivity via Spectral Reinforcement Learning

  • Michael Dann
  • John Thangarajah

In this paper we consider reinforcement learning tasks with progressive rewards; that is, tasks where the rewards tend to increase in magnitude over time. We hypothesise that this property may be problematic for value-based deep reinforcement learning agents, particularly if the agent must first succeed in relatively unrewarding regions of the task in order to reach more rewarding regions. To address this issue, we propose Spectral DQN, which decomposes the reward into frequencies such that the high frequencies only activate when large rewards are found. This allows the training loss to be balanced so that it gives more even weighting across small and large reward regions. In two domains with extreme reward progressivity, where standard value-based methods struggle significantly, Spectral DQN is able to make much farther progress. Moreover, when evaluated on a set of six standard Atari games that do not overtly favour the approach, Spectral DQN remains more than competitive: While it underperforms one of the benchmarks in a single game, it comfortably surpasses the benchmarks in three games. These results demonstrate that the approach is not overfit to its target problem, and suggest that Spectral DQN may have advantages beyond addressing reward progressivity.

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AAMAS Conference 2021 Conference Paper

Intention Progression using Quantitative Summary Information

  • Yuan Yao
  • Natasha Alechina
  • Brian Logan
  • John Thangarajah

A key problem for Belief-Desire-Intention (BDI) agents is intention progression, i. e. , which plans should be selected and how the execution of these plans should be interleaved so as to achieve the agent’s goals. Monte-Carlo Tree Search (MCTS) has been shown to be a promising approach to the intention progression problem, out-performing other approaches in the literature. However, MCTS relies on runtime simulation of possible interleavings of the plans in each intention, which may be computationally costly. In this paper, we introduce the notion of quantitative summary information which can be used to estimate the likelihood of conflicts between an agent’s intentions. We show how offline simulation can be used to precompute quantitative summary information prior to execution of the agent’s program, and how the precomputed summary information can be used at runtime to guide the expansion of the MCTS search tree and avoid unnecessary runtime simulation. We compare the performance of our approach with standard MCTS in a range of scenarios of increasing difficulty. The results suggest our approach can significantly improve the efficiency of MCTS in terms of the number of runtime simulations performed.

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IJCAI Conference 2021 Conference Paper

Multi-Agent Intention Progression with Black-Box Agents

  • Michael Dann
  • Yuan Yao
  • Brian Logan
  • John Thangarajah

We propose a new approach to intention progression in multi-agent settings where other agents are effectively black boxes. That is, while their goals are known, the precise programs used to achieve these goals are not known. In our approach, agents use an abstraction of their own program called a partially-ordered goal-plan tree (pGPT) to schedule their intentions and predict the actions of other agents. We show how a pGPT can be derived from the program of a BDI agent, and present an approach based on Monte Carlo Tree Search (MCTS) for scheduling an agent's intentions using pGPTs. We evaluate our pGPT-based approach in cooperative, selfish and adversarial multi-agent settings, and show that it out-performs MCTS-based scheduling where agents assume that other agents have the same program as themselves.

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AAMAS Conference 2021 Conference Paper

User and System Stories: An Agile Approach for Managing Requirements in AOSE

  • Sebastian Rodriguez
  • John Thangarajah
  • Michael Winikoff

The agile software development life cycle is widely used in industry today due to its highly flexible and iterative processes that facilitate rapid prototyping. There has been recent work in bringing concepts and processes from agile methodologies to agent-oriented software engineering (AOSE). We contribute to this effort by presenting in this paper a novel approach to capturing requirements of agent systems in AOSE using and extending agile concepts. In this paper, we propose to adopt and extend the well-known concept of User Stories to facilitate the development of agent systems. We introduce a novel concept, System Story, that defines requirements from the perspective of the system. These System Stories are refinements of User Stories and provide more intuitive mappings to agent concepts in the design and implementation. We show how our approach allows better traceability of requirements between stories and the different software development artifacts. We validate our proposal with a feature-based comparison to recent related work, and a preliminary user evaluation based on a drone simulation of a simple search and rescue case study.

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AAAI Conference 2020 Conference Paper

A Framework for Engineering Human/Agent Teaming Systems

  • Rick Evertsz
  • John Thangarajah

The increasing capabilities of autonomous systems offer the potential for more effective teaming with humans. Effective human/agent teaming is facilitated by a mutual understanding of the team objective and how that objective is decomposed into team roles. This paper presents a framework for engineering human/agent teams that delineates the key human/agent teaming components, using TDF-T diagrams to design the agents/teams and then present contextualised team cognition to the human team members at runtime. Our hypothesis is that this facilitates effective human/agent teaming by enhancing the human’s understanding of their role in the team and their coordination requirements. To evaluate this hypothesis we conducted a study with human participants using our user interface for the StarCraft strategy game, which presents pertinent, instantiated TDF-T diagrams to the human at runtime. The performance of human participants in the study indicates that their ability to work in concert with the non-player characters in the game is significantly enhanced by the timely presentation of a diagrammatic representation of team cognition.

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IJCAI Conference 2020 Conference Paper

Intention Progression under Uncertainty

  • Yuan Yao
  • Natasha Alechina
  • Brian Logan
  • John Thangarajah

A key problem in Belief-Desire-Intention agents is how an agent progresses its intentions, i. e. , which plans should be selected and how the execution of these plans should be interleaved so as to achieve the agent’s goals. Previous approaches to the intention progression problem assume the agent has perfect information about the state of the environment. However, in many real-world applications, an agent may be uncertain about whether an environment condition holds, and hence whether a particular plan is applicable or an action is executable. In this paper, we propose SAU, a Monte-Carlo Tree Search (MCTS)-based scheduler for intention progression problems where the agent’s beliefs are uncertain. We evaluate the performance of our approach experimentally by varying the degree of uncertainty in the agent’s beliefs. The results suggest that SAU is able to successfully achieve the agent’s goals even in settings where there is significant uncertainty in the agent’s beliefs.

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AAAI Conference 2019 Conference Paper

Deriving Subgoals Autonomously to Accelerate Learning in Sparse Reward Domains

  • Michael Dann
  • Fabio Zambetta
  • John Thangarajah

Sparse reward games, such as the infamous Montezuma’s Revenge, pose a significant challenge for Reinforcement Learning (RL) agents. Hierarchical RL, which promotes efficient exploration via subgoals, has shown promise in these games. However, existing agents rely either on human domain knowledge or slow autonomous methods to derive suitable subgoals. In this work, we describe a new, autonomous approach for deriving subgoals from raw pixels that is more efficient than competing methods. We propose a novel intrinsic reward scheme for exploiting the derived subgoals, applying it to three Atari games with sparse rewards. Our agent’s performance is comparable to that of state-of-the-art methods, demonstrating the usefulness of the subgoals found.

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IJCAI Conference 2017 Conference Paper

Agent Design Consistency Checking via Planning

  • Nitin Yadav
  • John Thangarajah
  • Sebastian Sardina

In this work we present a novel approach to check the consistency of agent designs (prior to any implementation) with respect to the requirements specifications via automated planning. This checking is essentially a search problem which makes planning technology an appropriate solution. We focus our work on BDI agent systems and the Prometheus design methodology in order to directly compare our approach to previous work. Our experiments in more than 16K random instances prove that the approach is more effective than previous ones proposed: it achieves higher coverage, lower run-time, and importantly, can handle loops in the agent detailed design and unbounded subgoal reasoning.

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AAMAS Conference 2017 Conference Paper

Progressing Intention Progression: A Call for a Goal-Plan Tree Contest

  • Brian Logan
  • John Thangarajah
  • Neil Yorke-Smith

User-supplied domain control knowledge in the form of hierarchically structured Goal-Plan Trees (GPTs) is at the heart of a number of approaches to reasoning about action. Reasoning with GPTs connects the AAMAS community with other communities such as automated planning, and forms the foundation for important reasoning capabilities, especially intention progression in Belief-Desire-Intention (BDI) agents. Research on GPTs has a long history but suffers from fragmentation and lack of common terminology, data formats, and enabling tools. One way to address this fragmentation is through a competition. Competitions are increasingly being used as a means to foster research and challenge the state of the art. For example, the AAMAS conference has a number of associated competitions, such as the Trading Agent Competition, while agent research is showcased at competitions such as RoboCup. We therefore issue a call for a Goal-Plan Tree Contest, with the ambition of drawing together a community and incentivizing research in intention progression.

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IJCAI Conference 2017 Conference Paper

Real-Time Navigation in Classical Platform Games via Skill Reuse

  • Michael Dann
  • Fabio Zambetta
  • John Thangarajah

In platform videogames, players are frequently tasked with solving medium-term navigation problems in order to gather items or powerups. Artificial agents must generally obtain some form of direct experience before they can solve such tasks. Experience is gained either through training runs, or by exploiting knowledge of the game's physics to generate detailed simulations. Human players, on the other hand, seem to look ahead in high-level, abstract steps. Motivated by human play, we introduce an approach that leverages not only abstract "skills", but also knowledge of what those skills can and cannot achieve. We apply this approach to Infinite Mario, where despite facing randomly generated, maze-like levels, our agent is capable of deriving complex plans in real-time, without relying on perfect knowledge of the game's physics.

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AAMAS Conference 2017 Conference Paper

Reusing Skills for First-Time Solution of Navigation Tasks in Platform Videogames

  • Michael Dann
  • Fabio Zambetta
  • John Thangarajah

We consider the problem of performing real-time navigation in domains where a“god’s eye view”is provided. One setting where this challenge arises is in platform videogames, occurring whenever the player wishes to reach an item or powerup on the current screen. Previous agents for these games rely on generating many low-level simulations or training runs for each fixed task. Human players, on the other hand, can solve navigation tasks at a high level by visualising sequences of abstract “skills”. Based on this intuition, we introduce a novel planning approach and apply it to Infinite Mario. Despite facing randomly generated, maze-like tasks, our agent is capable of deriving complex plans in real-time, without exploiting precise knowledge of the game’s code.

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ECAI Conference 2016 Conference Paper

Checking the Conformance of Requirements in Agent Designs Using ATL

  • Nitin Yadav
  • John Thangarajah

Intelligent agent systems built using the BDI model of agency have grown in popularity for implementing complex systems such as UAVs, military simulations, trading agents and intelligent games. The robust and flexible behaviours that these systems afford also makes testing the 'correctness' of these systems a non-trivial task. Whilst the main focus on existing work has been on checking the correctness of agent-programs, in this work we present an approach to formally verify agent-based designs for a particular BDI agent design methodology. The focus is on verifying whether the detailed design of the agents conform to the requirements specification. We present a sound and complete approach, formally verifiable properties, and an evaluation with respect to time and effectiveness.

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AAMAS Conference 2016 Conference Paper

Requirements Specification in the Prometheus Methodology via Activity Diagrams (JAAMAS Extended Abstract)

  • Yoosef Abushark
  • John Thangarajah
  • Tim Miller
  • Michael Winikoff
  • James Harland

In this work we extend a popular agent design methodology, Prometheus, and improve the understandability and maintainability of requirements by automatically generating UML activity diagrams from existing requirements models; namely scenarios and goal hierarchies. The approach is general to all the methodologies that support similar notions in specifying requirements.

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AAAI Conference 2016 Conference Paper

Robust Execution of BDI Agent Programs by Exploiting Synergies Between Intentions

  • Yuan Yao
  • Brian Logan
  • John Thangarajah

A key advantage the reactive planning approach adopted by BDI-based agents is the ability to recover from plan execution failures, and almost all BDI agent programming languages and platforms provide some form of failure handling mechanism. In general, these consist of simply choosing an alternative plan for the failed subgoal (e. g. , JACK, Jadex). In this paper, we propose an alternative approach to recovering from execution failures that relies on exploiting positive interactions between an agent’s intentions. A positive interaction occurs when the execution of an action in one intention assists the execution of actions in other intentions (e. g. , by (re)establishing their preconditions). We have implemented our approach in a scheduling algorithm for BDI agents which we call SP. The results of a preliminary empirical evaluation of SP suggest our approach out-performs existing failure handling mechanisms used by state-of-the-art BDI languages. Moreover, the computational overhead of SP is modest.

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JAAMAS Journal 2015 Journal Article

Preference-based reasoning in BDI agent systems

  • Simeon Visser
  • John Thangarajah
  • Frank Dignum

Abstract An important feature of BDI agent systems is number of different ways in which an agent can achieve its goals. The choice of means to achieve the goal in made by the system at run time, depending on contextual information that is not available in advance. In this article, we explore ways that the user of an agent system can specify preferences which can be incorporated into the BDI execution process and used to guide the choices made. For example, a user of a travel system can specify a preferred airline, or a particular kind of accommodation, and the system will use this information to satisfy the goal and preferences, if possible. Preferences are specified in terms of properties of goals and resource usage, and are used to make two types of decisions: (a) select a plan when there is a choice and (b) determine the order in which subgoals of a plan should be pursued when their order is not fixed by design. We have implemented our preference framework in Jadex, and provide detailed case studies within the context of a holiday travel agent application.

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ECAI Conference 2014 Conference Paper

Checking The Correctness of Agent Designs Against Model-Based Requirements

  • Yoosef B. Abushark
  • Michael Winikoff
  • Tim Miller 0001
  • James Harland
  • John Thangarajah

Agent systems are used for a wide range of applications, and techniques to detect and avoid defects in such systems are valuable. In particular, it is desirable to detect issues as early as possible in the software development lifecycle. We describe a technique for checking the plan structures of a BDI agent design against the requirements models, specified in terms of scenarios and goals. This approach is applicable at design time, not requiring source code. A lightweight evaluation demonstrates that a range of defects can be found using this technique.

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ECAI Conference 2014 Conference Paper

Quantifying the Completeness of Goals in BDI Agent Systems

  • John Thangarajah
  • James Harland
  • David N. Morley
  • Neil Yorke-Smith

Given the current set of intentions an autonomous agent may have, intention selection is the agent's decision which intention it should focus on next. Often, in the presence of conflicts, the agent has to choose between multiple intentions. One factor that may play a role in this deliberation is the level of completeness of the intentions. To that end, this paper provides pragmatic but principled mechanisms for quantifying the level of completeness of goals in a BDI-style agent. Our approach leverages previous work on resource and effects summarization but we go beyond by accommodating both dynamic resource summaries and goal effects, while also allowing a non-binary quantification of goal completeness. We demonstrate the computational approach on an autonomous robot case study.

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AAMAS Conference 2013 Conference Paper

A BDI Game Master Agent for Computer Role-Playing Games

  • Bao Vo Luong
  • John Thangarajah
  • Fabio Zambetta
  • Mahmud Hasan

In this paper we describe an approach for developing an intelligent game master (GM) for computer role-playing games. The role of the GM is to set up the game environment, manage the narrative flow and enforce the game rules whilst keeping the players engaged. Our approach is to use the popular Belief-Desire-Intention (BDI) model of agents to developing a GM.

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AAMAS Conference 2013 Conference Paper

AUML Protocols: From Specification to Detailed Design

  • Yoosef Abushark
  • John Thangarajah

In this work, we show how AUML protocol specifications in the Prometheus methodology can be automatically propagated to the detailed design of the methodology by creating appropriate artefacts. The approach is general to all design methodologies that follow the BDI model of agents.

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AAMAS Conference 2012 Conference Paper

Goal-Driven Approach To Open-Ended Dialogue Management using BDI Agents

  • Wilson Wong
  • Lawrence Cavedon
  • John Thangarajah
  • Lin Padgham

We describe a BDI (Belief, Desire, Intention) approach and architecture for a conversational virtual companion embodied as a child’s Toy. Our aim is to support both structured conversation-based activities (e. g. , story-telling, collaborative games) as well as more free-flowing, engaging dialogue with variation and some unpredictability. We argue that a goal-oriented approach to the agent’s conversational capabilities provides these competing capabilities.

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AAMAS Conference 2012 Conference Paper

Measuring Plan Coverage and Overlap for Agent Reasoning

  • John Thangarajah
  • Sebastian Sardina
  • Lin Padgham

In Belief Desire Intention (BDI) agent systems it is usual for goals to have a number of plans that are possible ways of achieving the goal, applicable in different situations, usually captured by a \emph{context condition}. In Agent Oriented Software Engineering it has been suggested that a designer should be conscious of whether a goal has \emph{complete coverage}, that is, is there some plan that is applicable for every situation. Similarly a designer should be conscious of \emph{overlap}, that is, for a given goal, are there situations where more than one plan could be applicable for achieving that goal. In this paper we further develop these notions in two ways, and then describe how they can be used both in agent reasoning and agent system development. Firstly, we replace the boolean value for basic coverage and overlap with numerical measures, and explain how these may be calculated. Secondly, we describe a measure that combines these basic measures, with the characteristics of the coverage/overlap in the goal-plan tree below a given goal. We then describe how these domain independent mesures can be used for both plan selection and intention selection, as well as for guidance in agent system design and development.

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AAMAS Conference 2012 Conference Paper

Revising Conflicting Intention Sets in BDI Agents

  • Steven Shapiro
  • Sebastian Sardina
  • John Thangarajah
  • Lawrence Cavedon
  • Lin Padgham

Autonomous agents typically have several goals they are pursuing simultaneously. Even if the goals themselves are not necessarily inconsistent, choices made about how to pursue each of these goals may well result in a set of intentions which are conflicting. A rational autonomous agent should be able to reason about and modify its set of intentions to take account of such issues. This paper presents the semantics of some preferences regarding modified sets of intentions. We look at the possibility of simply deleting some intention(s) but more importantly we also look at the possibility of modifying intentions, such that the goals will still be achieved but in a different way.

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AAMAS Conference 2011 Conference Paper

Reasoning About Preferences in BDI Agent Systems

  • Simeon Visser
  • John Thangarajah
  • James Harland

BDI agents often have to make decisions about which plan is used to achieve a goal, and in which order goals are to be achieved. In this paper we describe how to incorporate preferences (based on the LPP language) into the BDI execution model.

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IJCAI Conference 2011 Conference Paper

Reasoning about Preferences in Intelligent Agent Systems

  • Simeon Visser
  • John Thangarajah
  • James Harland

Agent systems based on the BDI paradigm need to make decisions about which plans are used to achieve their goals. Usually the choice of which plans to use to achieve a particular goal is left up to the system to determine. In this paper we show how preferences, which can be set by the user of the system, can be incorporated into the BDI execution process and used to guide the choices made.

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AAMAS Conference 2011 Conference Paper

Scenarios for System Requirements Traceability and Testing

  • John Thangarajah
  • Gaya Jayatilleke
  • Lin Padgham

Scenarios in current design methodologies, provide a natural way for the users to identify the inputs and outputs of the system revolving around a particular interaction process. A scenario typically consists of a sequence of steps which captures a particular run of the system and satisfies some aspect of the requirements. In this work we add additional structure to the scenarios used in the Prometheus agent development methodology. This additional structure then facilitates both traceability and automated testing. We describe our process for mapping the scenarios and their steps to the initial detailed design, where we then maintain the traceability as the design develops. The structured action lists that we define for both scenarios and their variations provides the basis for facilitating automated testing of system behavior. We describe how we use the newly defined structure within the scenarios to facilitate testing, describing how we automate test case generation, execution and analysis.

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AAMAS Conference 2010 Conference Paper

Eclipse-based Prometheus Design Tool

  • Hongyuan Sun
  • John Thangarajah
  • Lin Padgham

The Prometheus Design Tool (PDT) is a graphical tool that is used to design a Multi-Agent System following the Prometheus Methodology. This paper describes the latest version of PDT which is now integrated into the Eclipse platform, enabling the users to accomplish the full development life-cycle of an agent-oriented application in one IDE and also inherit the rich set of product development features that Eclipse provides. This version of PDT also aims to support simpler integration with tools from other AOSE methodologies where appropriate.

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AAMAS Conference 2008 Conference Paper

Automated Unit Testing Intelligent Agents in PDT

  • Zhiyong Zhang
  • John Thangarajah
  • Lin Padgham

The Prometheus Design Tool (PDT) is an agent development tool that supports the Prometheus design methodology and includes features like automated code generation. We enhance this tool by adding a feature that allows the automated unit testing of agents that are built from within PDT.

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AAAI Conference 2008 System Paper

Prometheus Design Tool

  • John Thangarajah

The Prometheus Design Tool (PDT) supports the structured design of intelligent agent systems. It supports the Prometheus methodology, but can also be used more generally. This paper outlines the tool and some of its many features.

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AAMAS Conference 2008 Conference Paper

Suspending and Resuming Tasks in Intelligent Agents

  • John Thangarajah
  • James Harland
  • David Morley
  • Neil Yorke-Smith

Intelligent agents designed to work in complex, dynamic environments must respond robustly and flexibly to environmental and circumstantial changes. An agent must be capable of deliberating about appropriate courses of action, which may include reprioritising goals, aborting particular tasks, or scheduling tasks in a particular order. This paper investigates the incorporation of a mechanism to suspend and reconsider tasks within a BDI-style architecture. Such an ability provides an agent designer greater flexibility to direct agent operation, and it offers a generic means for handling conflicts between tasks. We investigate conditions under which a goal or a plan may be suspended, the process for suspending it, and the appropriate behaviours upon resumption. We give an operational semantics for suspending tasks in terms of the abstract agent language CAN, thus providing a general mechanism that can be incorporated into any BDI-based agent programming language.

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AAMAS Conference 2007 Conference Paper

Aborting Tasks in BDI Agents

  • John Thangarajah
  • James Harland
  • David Morley
  • Neil Yorke-Smith

Intelligent agents that are intended to work in dynamic environments must be able to gracefully handle unsuccessful tasks and plans. In addition, such agents should be able to make rational decisions about an appropriate course of action, which may include aborting a task or plan, either as a result of the agent's own deliberations, or potentially at the request of another agent. In this paper we investigate the incorporation of aborts into a BDI-style architecture. We discuss some conditions under which aborting a task or plan is appropriate, and how to determine the consequences of such a decision. We augment each plan with an optional abort-method, analogous to the failure method found in some agent programming languages. We provide an operational semantics for the execution cycle in the presence of aborts in the abstract agent language CAN, which enables us to specify a BDI-based execution model without limiting our attention to a particular agent system (such as JACK, Jadex, Jason, or SPARK). A key technical challenge we address is the presence of parallel execution threads and of sub-tasks, which require the agent to ensure that the abort methods for each plan are carried out in an appropriate sequence.

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AAMAS Conference 2007 Conference Paper

AUML Protocols and Code Generation in the Prometheus Design Tool

  • Lin Padgham
  • John Thangarajah
  • Michael Winikoff

Prometheus is an agent-oriented software engineering methodology. The Prometheus Design Tool (PDT) is a software tool that supports a designer who is using the Prometheus methodology. PDT has recently been extended with two significant new features: support for Agent UML interaction protocols, and code generation.

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IJCAI Conference 2003 Conference Paper

Detecting & Avoiding Interference Between Goals in Intelligent Agents

  • John Thangarajah
  • Lin Padgham
  • Michael Winikoff

Pro-active agents typically have multiple simultaneous goals. These may interact with each other both positively and negatively. In this paper we provide a mechanism allowing agents to detect and avoid a particular kind of negative interaction where the effects of one goal undo conditions that must be protected for successful pursuit of another goal. In order to detect such interactions we maintain summary information about the definite and potential conditional requirements and resulting effects of goals and their associated plans. We use these summaries to guard protected conditions by scheduling the execution of goals and plan steps. The algorithms and data structures developed allow agents to act rationally instead of blindly pursuing goals that will conflict.

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