CAMP-BDI: an approach for multiagent systems robustness through capability-aware agents maintaining plans

Rational agent behaviour is frequently achieved through the use of plans, particularly within the widely used BDI (Belief-Desire-Intention) model for intelligent agents. As a consequence, preventing or handling failure of planned activity is a vital component in building robust multiagent systems; this is especially true in realistic environments, where unpredictable exogenous change during plan execution may threaten intended activities. Although reactive approaches can be employed to respond to activity failure through replanning or plan-repair, failure may have debilitative effects that act to stymie recovery and, potentially, hinder subsequent activity. A further factor is that BDI agents typically employ deterministic world and plan models, as probabilistic planning methods are typical intractable in realistically complex environments. However, deterministic operator preconditions may fail to represent world states which increase the risk of activity failure. The primary contribution of this thesis is the algorithmic design of the CAMP-BDI (Capability Aware, Maintaining Plans) approach; a modification of the BDI reasoning cycle which provides agents with beliefs and introspective reasoning to anticipate increased risk of failure and pro-actively modify intended plans in response. We define a capability meta-knowledge model, providing information to identify and address threats to activity success using precondition modelling and quantitative quality estimation. This also facilitates semantic-independent communication of capability information for general advertisement and of dependency information - we define use of the latter, within a structured messaging approach, to extend local agent algorithms towards decentralized, distributed robustness. Finally, we define a policy based approach for dynamic modification of maintenance behaviour, allowing response to observations made during runtime and with potential to improve re-usability of agents in alternate environments. An implementation of CAMP-BDI is compared against an equivalent reactive system through experimentation in multiple perturbation configurations, using a logistics domain. Our empirical evaluation indicates CAMP-BDI has significant benefit if activity failure carries a strong risk of debilitative consequence

Context-aware Plan Repair in Environments shared by Multiple Agents

[ES] La monitorización de la ejecución de un plan es crucial para un agente autónomo que realiza su labor en un entorno dinámico, pues influye en su capacidad de reaccionar ante los cambios. Mientras ejecuta su plan puede sufrir un fallo y, en su esfuerzo por solucionarlo, puede interferir sin saberlo con otros agentes que operan en su mismo entorno. Por otra parte, para actuar racionalmente es necesario que el agente sea consciente del contexto y pueda recopilar y ampliar su información a partir de lo que percibe para poder compensar su conocimiento previo parcial o incorrecto del problema y lograr el mejor resultado posible ante las nuevas situaciones que aparecen. El trabajo realizado en esta tesis permite a los agentes autónomos ejecutar sus planes en un entorno dinámico y adaptarse a eventos inesperados y circunstancias desconocidas. Pueden utilizar su percepción del contexto para proporcionar respuestas deliberativas conscientes y ser capaces así de aprovechar las oportunidades que surgen o reparar los fallos sin perturbar a otros agentes. Este trabajo se centra en el desarrollo de una arquitectura independiente del dominio capaz de manejar las necesidades de agentes con este tipo de comportamiento autónomo. Los tres pilares de la arquitectura propuesta los forman el sistema inteligente para la simulación de la ejecución en entornos dinámicos, la adquisición de conocimiento consciente del contexto para ampliar la base de datos del agente y la reparación de planes ante fallos u oportunidades tratando de interferir lo mínimo con los planes de otros agentes. El sistema inteligente de simulación de la ejecución permite al agente representar el plan en una línea de tiempo, actualizar periódicamente su estado interno con información del mundo real y disparar nuevos eventos en momentos concretos. Los eventos se procesan en el contexto del plan; si se detecta un error, el simulador reformula el problema de planificación, invoca de nuevo al planificador y reanuda la ejecución. El simulador es una aplicación de consola y ofrece una interfaz gráfica diseñada específicamente para una aplicación inteligente de turismo. El módulo de adquisición de conocimiento sensible al contexto utiliza operaciones semánticas para aumentar dinámicamente la lista predefinida de tipos de objetos de la tarea de planificación con nuevos tipos relevantes. Esto permite que el agente sea consciente de su entorno, enriquezca el modelo de su tarea y pueda razonar a partir de un conocimiento incompleto. Con todo esto se consigue potenciar la autonomía del sistema y la conciencia del contexto. La novedosa estrategia de reparación de planes le permite a un agente reparar su plan al detectar un fallo de manera responsable con el resto de agentes que comparten su mismo entorno de ejecución. El agente utiliza una nueva métrica, el compromiso del plan, como función heurística para guiar la búsqueda hacia un plan solución comprometido con el plan original, en el sentido de que se trata de respetar los compromisos adquiridos con otros agentes al mismo tiempo que se alcanzan los objetivos originales. En consecuencia, la comunidad de agentes sufrirá menos fallos por cambios bruscos en el entorno o requerirá menos tiempo para ejecutar las acciones correctoras si el fallo es inevitable. Estos tres módulos han sido desarrollados y evaluados en varias aplicaciones como un asistente turístico, una agencia de reparación de electrodomésticos y un asistente del hogar.[CA] El monitoratge de l'execució d'un pla és crucial per a un agent autònom que realitza la seua labor en un entorn dinàmic, perquè influeix en la seua capacitat de reaccionar davant els canvis. Mentre executa el seu pla pot patir una fallada i, en el seu esforç per solucionar-lo, pot interferir sense saber-ho amb altres agents que operen en el seu mateix entorn. D'altra banda, per a actuar racionalment és necessari que l'agent siga conscient del context i puga recopilar i ampliar la seua informació a partir del que percep per a poder compensar el seu coneixement previ parcial o incorrecte del problema i aconseguir el millor resultat possible davant les noves situacions que apareixen. El treball realitzat en aquesta tesi permet als agents autònoms executar els seus plans en un entorn dinàmic i adaptar-se a esdeveniments inesperats i circumstàncies desconegudes. Poden utilitzar la seua percepció del context per a proporcionar respostes deliberatives conscients i ser capaces així d'aprofitar les oportunitats que sorgeixen o reparar les fallades sense pertorbar a altres agents. Aquest treball se centra en el desenvolupament d'una arquitectura independent del domini capaç de manejar les necessitats d'agents amb aquesta mena de comportament autònom. Els tres pilars de l'arquitectura proposada els formen el sistema intel·ligent per a la simulació de l'execució en entorns dinàmics, l'adquisició de coneixement conscient del context per a ampliar la base de dades de l'agent i la reparació de plans davant fallades o oportunitats tractant d'interferir el mínim amb els plans d'altres agents. El sistema intel·ligent de simulació de l'execució permet a l'agent representar el pla en una línia de temps, actualitzar periòdicament el seu estat intern amb informació del món real i disparar nous esdeveniments en moments concrets. Els esdeveniments es processen en el context del pla; si es detecta un error, el simulador reformula el problema de planificació, invoca de nou al planificador i reprén l'execució. El simulador és una aplicació de consola i ofereix una interfície gràfica dissenyada específicament per a una aplicació intel·ligent de turisme. El mòdul d'adquisició de coneixement sensible al context utilitza operacions semàntiques per a augmentar dinàmicament la llista predefinida de tipus d'objectes de la tasca de planificació amb nous tipus rellevants. Això permet que l'agent siga conscient del seu entorn, enriquisca el model de la seua tasca i puga raonar a partir d'un coneixement incomplet. Amb tot això s'aconsegueix potenciar l'autonomia del sistema i la consciència del context. La nova estratègia de reparació de plans li permet a un agent reparar el seu pla en detectar una fallada de manera responsable amb la resta d'agents que comparteixen el seu mateix entorn d'execució. L'agent utilitza una nova mètrica, el compromís del pla, com a funció heurística per a guiar la cerca cap a un pla solució compromés amb el pla original, en el sentit que es tracta de respectar els compromisos adquirits amb altres agents al mateix temps que s'aconsegueixen els objectius originals. En conseqüència, la comunitat d'agents patirà menys fallades per canvis bruscos en l'entorn o requerirà menys temps per a executar les accions correctores si la fallada és inevitable. Aquests tres mòduls han sigut desenvolupats i avaluats en diverses aplicacions com un assistent turístic, una agència de reparació d'electrodomèstics i un assistent de la llar.[EN] Execution Monitoring is crucial for the success of an autonomous agent executing a plan in a dynamic environment as it influences its ability to react to changes. While executing its plan in a dynamic world, it may suffer a failure and, in its endeavour to fix the problem, it may unknowingly disrupt other agents operating in the same environment. Additionally, being rational requires the agent to be context-aware, gather information and extend what is known from what is perceived to compensate for partial or incorrect prior knowledge and achieve the best possible outcome in various novel situations. The work carried out in this PhD thesis allows the autonomous agents executing a plan in a dynamic environment to adapt to unexpected events and unfamiliar circumstances, utilise their perception of context and provide context-aware deliberative responses for seizing an opportunity or repairing a failure without disrupting other agents. This work is focused on developing a domain-independent architecture capable of handling the requirements of such autonomous behaviour. The architecture pillars are the intelligent system for execution simulation in a dynamic environment, the context-aware knowledge acquisition for planning applications and the plan commitment repair. The intelligent system for execution simulation in a dynamic environment allows the agent to transform the plan into a timeline, periodically update its internal state with real-world information and create timed events. Events are processed in the context of the plan; if a failure occurs, the simulator reformulates the planning problem, reinvokes a planner and resumes the execution. The simulator is a console application and has a GUI designed specifically for smart tourism. The context-aware knowledge acquisition module utilises semantic operations to dynamically augment the predefined list of object types of the planning task with relevant new object types. This allows the agent to be context-aware of the environment and the task and reason with incomplete knowledge, boosting the system's autonomy and context-awareness. The novel plan commitment repair strategy among multiple agents sharing the same execution environment allows the agent to repair its plan responsibly when a failure is detected. The agent utilises a new metric, plan commitment, as a heuristic to guide the search for the most committed repair plan to the original plan from the perspective of commitments made to other agents whilst achieving the original goals. Consequently, the community of agents will suffer fewer failures due to the sudden changes or will have less lost time if the failure is inevitable. All these developed modules were investigated and evaluated in several applications, such as a tourist assistant, a kitchen appliance repair agency and a living home assistant.Babli, M. (2023). Context-aware Plan Repair in Environments shared by Multiple Agents [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19868

Goal Reasoning: Papers from the ACS workshop

This technical report contains the 11 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2013 Conference on Advances in Cognitive Systems (ACS-13) in Baltimore, Maryland on 14 December 2013. This is the third in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy while the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012. Our objective for holding this meeting was to encourage researchers to share information on the study, development, integration, evaluation, and application of techniques related to goal reasoning, which concerns the ability of an intelligent agent to reason about, formulate, select, and manage its goals/objectives. Goal reasoning differs from frameworks in which agents are told what goals to achieve, and possibly how goals can be decomposed into subgoals, but not how to dynamically and autonomously decide what goals they should pursue. This constraint can be limiting for agents that solve tasks in complex environments when it is not feasible to manually engineer/encode complete knowledge of what goal(s) should be pursued for every conceivable state. Yet, in such environments, states can be reached in which actions can fail, opportunities can arise, and events can otherwise take place that strongly motivate changing the goal(s) that the agent is currently trying to achieve. This topic is not new; researchers in several areas have studied goal reasoning (e.g., in the context of cognitive architectures, automated planning, game AI, and robotics). However, it has infrequently been the focus of intensive study, and (to our knowledge) no other series of meetings has focused specifically on goal reasoning. As shown in these papers, providing an agent with the ability to reason about its goals can increase performance measures for some tasks. Recent advances in hardware and software platforms (involving the availability of interesting/complex simulators or databases) have increasingly permitted the application of intelligent agents to tasks that involve partially observable and dynamically-updated states (e.g., due to unpredictable exogenous events), stochastic actions, multiple (cooperating, neutral, or adversarial) agents, and other complexities. Thus, this is an appropriate time to foster dialogue among researchers with interests in goal reasoning. Research on goal reasoning is still in its early stages; no mature application of it yet exists (e.g., for controlling autonomous unmanned vehicles or in a deployed decision aid). However, it appears to have a bright future. For example, leaders in the automated planning community have specifically acknowledged that goal reasoning has a prominent role among intelligent agents that act on their own plans, and it is gathering increasing attention from roboticists and cognitive systems researchers. In addition to a survey, the papers in this workshop relate to, among other topics, cognitive architectures and models, environment modeling, game AI, machine learning, meta-reasoning, planning, selfmotivated systems, simulation, and vehicle control. The authors discuss a wide range of issues pertaining to goal reasoning, including representations and reasoning methods for dynamically revising goal priorities. We hope that readers will find that this theme for enhancing agent autonomy to be appealing and relevant to their own interests, and that these papers will spur further investigations on this important yet (mostly) understudied topic

Supporting IT Service Fault Recovery with an Automated Planning Method

Despite advances in software and hardware technologies, faults are still inevitable in a highly-dependent, human-engineered and administrated IT environment. Given the critical role of IT services today, it is imperative that faults, having once occurred, have to be dealt with eciently and eeffectively to avoid or reduce the actual losses. Nevertheless, the complexities of current IT services, e.g., with regard to their scales, heterogeneity and highly dynamic infrastructures, make the recovery operation a challenging task for operators. Such complexities will eventually outgrow the human capability to manage them. Such diculty is augmented by the fact that there are few well-devised methods available to support fault recovery. To tackle this issue, this thesis aims at providing a computer-aided approach to assist operators with fault recovery planning and, consequently, to increase the eciency of recovery activities.We propose a generic framework based on the automated planning theory to generate plans for recoveries of IT services. At the heart of the framework is a planning component. Assisted by the other participants in the framework, the planning component aggregates the relevant information and computes recovery steps accordingly. The main idea behind the planning component is to sustain the planning operations with automated planning techniques, which is one of the research fields of articial intelligence. Provided with a general planning model, we show theoretically that the service fault recovery problem can be indeed solved by automated planning techniques. The relationship between a planning problem and a fault recovery problem is shown by means of reduction between these problems. After an extensive investigation, we choose a planning paradigm that based on Hierarchical Task Networks (HTN) as the guideline for the design of our main planning algorithm called H2MAP. To sustain the operation of the planner, a set of components revolving around the planning component is provided. These components are responsible for tasks such as translation between dierent knowledge formats, persistent storage of planning knowledge and communication with external systems. To ensure extendibility in our design, we apply dierent design patterns for the components. We sketch and discuss the technical aspects of implementations of the core components. Finally, as proof of the concept, the framework is instantiated to two distinguishing application scenarios

Network-centric automated planning and execution

Web services provide interoperability to network hosts with different capabilities. Complex tasks can be performed by composing services, assuming sufficient service descriptions are provided. Researchers are just beginning to realize the importance of accounting for network properties during automated service composition. The work presented in this thesis considers dynamic, heterogeneous networks—one type of network-centric environment.The purpose of this research is to improve network-centric service composition. This is accomplished by converting the service composition problem to an automated planning under uncertainty problem and by reasoning about network properties at various stages of the planning process. This thesis presents a method of improving the agents’ ability to construct, execute, and monitor plans in network-centric environments.There are two main contributions of this thesis: 1) generating qualitatively-different plans and 2) creating network-aware agents. As part of the former contribution, this thesis presents a comparison of methods used to create classical planning domains for distributed service composition problems. The other part of this contribution is an algorithm for guiding a plan-space planner to create qualitatively-different plans based on domain-dependent and network-centric plan evaluations. The second contribution pertains to network-awareness, which agents exhibit by reacting to changes in network conditions. This thesis describes methods of incorporating network-awareness into agents that 1) create plans, 2) execute plans, and 3) monitor plan execution.Experiments to validate the aforementioned contributions are presented in the context of an Improvised Explosive Device (IED) detection scenario. Several locations are monitored for IEDs using a variety of techniques including manual searching and visual change detection, as well as a variety of resources including humans, robots, and unmanned aerial vehicles (UAVs). Empirical results indicate that incorporating network-awareness into agents in dynamic, heterogeneous networks improves the overall service composition performance and effectiveness.M.S., Computer Science -- Drexel University, 200

Technological roadmap on AI planning and scheduling

At the beginning of the new century, Information Technologies had become basic and indispensable constituents of the production and preparation processes for all kinds of goods and services and with that are largely influencing both the working and private life of nearly every citizen. This development will continue and even further grow with the continually increasing use of the Internet in production, business, science, education, and everyday societal and private undertaking. Recent years have shown, however, that a dramatic enhancement of software capabilities is required, when aiming to continuously provide advanced and competitive products and services in all these fast developing sectors. It includes the development of intelligent systems – systems that are more autonomous, flexible, and robust than today’s conventional software. Intelligent Planning and Scheduling is a key enabling technology for intelligent systems. It has been developed and matured over the last three decades and has successfully been employed for a variety of applications in commerce, industry, education, medicine, public transport, defense, and government. This document reviews the state-of-the-art in key application and technical areas of Intelligent Planning and Scheduling. It identifies the most important research, development, and technology transfer efforts required in the coming 3 to 10 years and shows the way forward to meet these challenges in the short-, medium- and longer-term future. The roadmap has been developed under the regime of PLANET – the European Network of Excellence in AI Planning. This network, established by the European Commission in 1998, is the co-ordinating framework for research, development, and technology transfer in the field of Intelligent Planning and Scheduling in Europe. A large number of people have contributed to this document including the members of PLANET non- European international experts, and a number of independent expert peer reviewers. All of them are acknowledged in a separate section of this document. Intelligent Planning and Scheduling is a far-reaching technology. Accepting the challenges and progressing along the directions pointed out in this roadmap will enable a new generation of intelligent application systems in a wide variety of industrial, commercial, public, and private sectors

Aeronautical engineering: A continuing bibliography with indexes (supplement 323)

This bibliography lists 518 reports, articles, and other documents introduced into the NASA scientific and technical information system in November 1995. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Command and Control Systems for Search and Rescue Robots

The novel application of unmanned systems in the domain of humanitarian Search and Rescue (SAR) operations has created a need to develop specific multi-Robot Command and Control (RC2) systems. This societal application of robotics requires human-robot interfaces for controlling a large fleet of heterogeneous robots deployed in multiple domains of operation (ground, aerial and marine). This chapter provides an overview of the Command, Control and Intelligence (C2I) system developed within the scope of Integrated Components for Assisted Rescue and Unmanned Search operations (ICARUS). The life cycle of the system begins with a description of use cases and the deployment scenarios in collaboration with SAR teams as end-users. This is followed by an illustration of the system design and architecture, core technologies used in implementing the C2I, iterative integration phases with field deployments for evaluating and improving the system. The main subcomponents consist of a central Mission Planning and Coordination System (MPCS), field Robot Command and Control (RC2) subsystems with a portable force-feedback exoskeleton interface for robot arm tele-manipulation and field mobile devices. The distribution of these C2I subsystems with their communication links for unmanned SAR operations is described in detail. Field demonstrations of the C2I system with SAR personnel assisted by unmanned systems provide an outlook for implementing such systems into mainstream SAR operations in the future

Acting and Learning with Goal and Task Decomposition

Two central problems of creating artificial intelligent agents that can operate in the human world are learning the necessary knowledge to achieve routine tasks, and using that knowledge effectively in a complex and unpredictable domain. The thesis argues that an important part of this domain knowledge should be represented in the form of decomposition rules that decompose tasks into subgoals. The thesis presents HOPPER, an implemented planning system that uses decomposition rules and a least-commitment decomposition strategy that strikes a balance between reactive and deliberative planning. Like reactive planners, HOPPER is able to robustly handle and recover from unexpected events with minimal disruption to its plan. Like deliberative planners, it is also able to plan ahead to take advantage of opportunities to interleave and shorten its sub-plans. The thesis also presents TADPOLE, an implemented learning system that learns both the structure and preconditions of new decomposition rules from a small number of lessons demonstrated by a teacher. It learns by parsing and interpreting the teacher’s behaviour in terms of decomposition rules it already knows. It extends its rule set by filling in the holes in its parses of the teacher’s lessons. Both HOPPER and TADPOLE have been evaluated together in two different domains: a kitchen domain that emphasizes complexity, and a logistics domain that emphasizes plan efficiency. Every rule used by HOPPER was learned by TADPOLE and every rule learned by TADPOLE was successfully used by HOPPER to achieve various tasks, showing that TADPOLE is able to learn effective decomposition rules from minimal lessons from a teacher, and that HOPPER is able to robustly make use of them even in the face of unexpected events