Theodorou, Andreas

Current software for AI development requires the use of programming languages to develop intelligent agents. This can be disadvantageous for AI designers, as their work needs to be debugged and treated as a generic piece of software code. Moreover, such approaches are designed for experts, requiring a steep initial learning curve, as they are tailored for programmers. This can be disadvantageous for implementing transparent inspection of agents, as additional work is needed to expose and represent information to end users. We are working towards the development of a new editor, ABOD3, shown in fig.~\ref{abod3}. It allows the graphical visualisation of BOD-based plans, including its two major derivatives: POSH and Instinct. The new editor is designed to allow not only the development of reactive plans, but also to debug such plans in real time to reduce the time required to develop an agent. This allows the development and testing of plans from a same application.The editor provides a user-customisable user interface (UI) aimed at supporting both the development and debug of agents. Plan elements, their subtrees, and debugging-related information can be hidden, to allow different levels of abstraction and present only relevant information. The graphical representation of the plan can be generated automatically, and the user can override its default layout by moving elements to suit his needs and preferences.The simple UI and customisation allows the editor to be employed not only as a developer's tool, but also to present transparency related information to the end-user to help them develop more accurate mental models of the agent. Alpha testers have already used ABOD3 in experiments to determine the effects of transparency on the mental models formed by humans. Their experiments consisted of a non-humanoid robot, powered by the BOD-based Instinct reactive planner. They have demonstrated that subjects can show marked improvement in the accuracy of their mental model of a robot observed, if they also see an accompanying display of the robot's real-time decision making as provided by ABOD3. They concluded that providing transparency information by using ABOD3 does help users to understand the behaviour of the robot, calibrating their expectations.We plan to continue developing this new editor, implementing debug functions such as ``fast-forward'' in pre-recorded log files and usage of breakpoints in real-time. A transparent agent, with an inspectable decision-making mechanism, could also be debugged in a similar manner to the way in which traditional, non-intelligent software is commonly debugged. The developer would be able to see which actions the agent is selecting, why this is happening, and how it moves from one action to the other. This is similar to the way in which popular Integrated Development Environments (IDEs) provide options to follow different streams of code with debug points. Moreover, we will enhance its plan design capabilities by introducing new views, to view and edit specific types of plan-elements and through a public beta testing to gather feedback by both experienced and inexperienced AI developers

OPUS

        Citation for published version:Theodorou, A 2017, 'ABOD3: A Graphical Visualization and Real-Time Debugging Tool for BOD Agents', CEURWorkshop Proceedings, vol. 1855, pp. 60-61.Publication date:2017Document VersionPublisher's PDF, also known as Version of recordLink to publicationUniversity of BathGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Download date: 13. May. 2019ABOD3A Graphical Visualisation and Real-TimeDebugging Tool for BOD AgentsAndreas TheodorouDepartment of Computer ScienceUniversity of BathBath, BA2 7AY UKEmail: a.theodorou@bath.ac.ukI. INTRODUCTIONCurrent software for AI development requires the use ofprogramming languages to develop intelligent agents. Thiscan be disadvantageous for AI designers, as their work needsto be debugged and treated as a generic piece of softwarecode. Moreover, such approaches are designed for experts;often requiring a steep initial learning curve, as they aretailored for programmers. This can be also disadvantageousfor implementing transparency to agents, an important ethicalconsideration [1], [2], as additional work is needed to exposeand represent information to end users.We are working towards the development of a new editor,ABOD3. It allows the graphical visualisation of BehaviourOriented Design based plans [3], including its two majorderivatives: Parallel-rooted, Ordered Slip-stack Hierarchical(POSH) and Instinct [4].The new editor is designed to allow not only the develop-ment of reactive plans, but also to debug such plans in real timeto reduce the time required to develop an agent. This allowsthe development and testing of plans from a same application.II. BEHAVIOUR ORIENTED DESIGNBehaviour Oriented Design (BOD) [5], [6] takes inspirationboth from the well-established programming paradigm object-oriented design (ODD) and Behaviour-Base AI (BBAI) [7],to provide a concrete architecture for developing complete,complex agents (CCAs), with multiple conflicting goals andmutual-exclusive means of achieving those goals. BBAI wasfirst introduced by Brooks [7], where intelligence is decom-posed into simple, robust modules, each expressing capabil-ities, actions such as movement, rather than mental entitiessuch as knowledge and thought.Bryson’s BOD is a cognitive architecture which promotesbehaviour decomposition, code modularity and reuse, makingthe development of intelligent agents easier. BOD describesthe agent’s behaviour into multiple modules forming a be-haviour library. Each module can have a set of expressedbehaviours called acts, actions, perceptions, learning, andmemory. Behaviour modules also store their own memories,i.e. sensory experiences.Action selection is forced by competition for resources. Ifno such competition exists, the behaviour modules are able towork in parallel enforcing the long-term goals of the agentA. POSHPOSH planning is the action selection for reactive planningderivative of BOD. POSH combines faster response timessimilar to reactive approaches for BBAI with goal-directedplans. A POSH plan consists of the following plan elements:1) Drive Collection (DC): It contains a set of Drives andis responsible for giving attention to the highest priorityDrive. To allow the agent to shift and focus attention,only one Drive can be active in any given cycle.2) Drive (D): Allows for the design and pursuit of a specificbehaviour as it maintains its execution state. The triggeris a precondition, a primitive plan element called Sense,determining if the drive should be executed by using asensory input.3) Competence (C): Contains one or more CompetenceElements (CE), each of which has a priority and areleaser. A CE may trigger the execution of anotherCompetence, an Action Pattern, or a single Action.4) Action Pattern (AP): Used to reduce the computationalcomplexity of search within the plan space and to allowa coordinated fixed sequential execution of a set ofActions.B. InstinctThe Instinct Planner is a reactive planner based on thePOSH planner. It includes several enhancements taken frommore recent papers extending POSH [8]. In an Instinct plan,an AP contains one or more Action Pattern Elements (APE),each of which has a priority, and links to a specific Action,Competence, or another AP.III. THE PLAN EDITORThe editor provides a customisable user interface (UI) aimedat supporting both the development and debugging of agents.Plan elements, their subtrees, and debugging-related informa-tion can be hidden, to allow different levels of abstraction andpresent only relevant information. The graphical representationof the plan can be generated automatically, and the user canoverride its default layout by moving elements to suit his needsand preferences. The simple UI and customisation allows theeditor to be employed not only as a developer’s tool, but alsoto present transparency related information to the end users,helping them to develop more accurate mental models of theagent.Alpha testers have already used ABOD3 in experiments todetermine the effects of transparency on the mental modelsformed by humans [9], [10]. Their experiments consisted ofa non-humanoid robot, powered by the BOD-based Instinctreactive planner. They have demonstrated that subjects, if theyalso see an accompanying display of the robot’s real-timedecision making as provided by ABOD3, can show markedimprovement in the accuracy of their mental model of arobot observed. They concluded that providing transparencyinformation by using ABOD3 does help users to understandthe behaviour of the robot, calibrating their expectations.Plan elements flash as they are called by the planner andglow based on the number of recent invocations of thatelement. Plan elements without any recent invocations startdimming down, over a user-defined interval, until they returnback to their initial state. This offers abstracted backtrackingof the calls. Sense information and progress towards a goal aredisplayed. Finally, ABOD3 provides integration with videos ofthe agent in action, synchronised by the time signature withinthe recorded transparency feed.Fig. 1. The ABOD3 Graphical Transparency Tool displaying an Instinct planin debugging mode. The highlighted elements are the ones recently called bythe planner. The intensity of the glow indicates the number of recent calls.ABODE3 provides an API that allows the editor to connectwith planners, presenting debugging information in real time.For example, it can connect to the Instinct planner by using abuilt-in TCP/IP server, see Figure 2.IV. CONCLUSIONWe plan to continue developing this new editor, implement-ing debug functions such as “fast-forward” in pre-recordedlog files and usage of breakpoints in real-time. A transparentagent, with an inspectable decision-making mechanism, couldFig. 2. System Architecture Diagram of ABOD3, showing its modular design.All of ABOD3 was written in Java, to ensure cross-platform compatibility.APIs allows the expansion of the software to support additional BOD plannersfor real-time debugging, BOD based plans, and User Interfaces. The editoraims, through personalisation, to support roboticists, games AI developers,and even end users.also be debugged in a similar manner to the way in whichtraditional, non-intelligent software is commonly debugged.The developer would be able to see which actions the agentis selecting, why this is happening, and how it moves fromone action to the other. This is similar to the way in whichpopular Integrated Development Environments (IDEs) provideoptions to follow different streams of code with debug points.Moreover, we will enhance its plan design capabilities byintroducing new views, to view and edit specific types of plan-elements and through a public beta testing to gather feedbackby both experienced and inexperienced AI developers.REFERENCES[1] A. Theodorou, R. H. Wortham, and J. J. Bryson, “Designing andimplementing transparency for real time inspection of autonomousrobots,” Connection Science, vol. 29, 2017.[2] R. H. Wortham, A. Theodorou, and J. J. Bryson, “Robot Transparency ,Trust and Utility,” in ASIB 2016: EPSRC Principles of Robotics, 2016.[3] J. Bryson, “The behavior-oriented design of modular agent intelligence,”in System, 2002, vol. 2592, pp. 61–76.[4] R. H. Wortham, S. E. Gaudl, and J. J. Bryson, “Instinct : A BiologicallyInspired Reactive Planner for Embedded Environments,” in Proceedingsof ICAPS 2016 PlanRob Workshop, 2016.[5] J. Bryson and L. A. Stein, “Intelligence by Design : Principles ofModularity and Coordination for Engineering Complex Adaptive Agentsby,” no. September 2001, 2001.[6] J. J. Bryson, “Action selection and individuation in agent based mod-elling,” in Proceedings of Agent 2003: Challenges in Social Simulation,D. L. Sallach and C. Macal, Eds. Argonne, IL: Argonne NationalLaboratory, 2003, pp. 317–330.[7] R. A. Brooks, “Intelligence Without Representation,” Artificial Intelli-gence, vol. 47, no. 1, pp. 139–159, 1991.[8] S. Gaudl and J. J. Bryson, “The Extended Ramp Goal Module:Low-Cost Behaviour Arbitration for Real-Time Controllers based onBiological Models of Dopamine Cells,” Computational Intelligence inGames 2014, 2014. [Online]. Available: http://opus.bath.ac.uk/40056/[9] R. H. Wortham, A. Theodorou, and J. J. Bryson, “What Does theRobot Think? Transparency as a Fundamental Design Requirementfor Intelligent System,” in IJCAI-2016 Ethics for Artificial IntelligenceWorkshop, 2016.[10] R. Wortham, A. Theodorou, and J. J. Bryson, “Improving robottransparency:real-time visualisation of robot ai substantially improvesunderstanding in naive observers,” in IEEE RO-MAN 2017, 2017.

ABOD3: A Graphical Visualization and Real-Time Debugging Tool for BOD Agents

https://purehost.bath.ac.uk/ws/files/155965047/Theodorou_ABOD3.pdf

ABOD3: A Graphical Visualization and Real-Time Debugging Tool for BOD Agents

Abstract

Similar works

Full text

Available Versions

OPUS