Integrating conversational case retrieval with generative planning

Advances in Case-Based Reasoning Research and Development: Proceedings of the 5th European Workshop on Case-Based Reasoning, EWCBR 2000, pp. 210-221.Some problem-solving tasks are amenable to integrated case retrieval and generative planning techniques. This is certainly true for some decision support tasks, in which a user controls the problem-solving process but cannot provide a complete domain theory. Unfortunately, existing integrations are either non-interactive or require a complete domain theory and/or complete world state to produce acceptable plans preventing them from being easily used in these situations. We describe a novel integrated algorithm, named SiN, that is interactive and does not require a complete domain theory or complete world state. SiN users leverage a conversational case retriever to focus both partial world state acquisition and plan generation. We highlight the benefits of SiN (e.g. quadratically fewer cases needed) in an experimental study using a new travel planning domain

An intelligent lessons learned process

Paper presented at The 12th International Symposium, ISMIS 2000: pp. 358-367.A learned lesson, in the context of a pre-defined organizational process, summarizes an experience that should be used to modify that process, under the conditions for which that lesson applies. To promote lesson reuse, many organizations employ lessons learned processes, which define how to collect, validate, store, and disseminate lessons among their personnel, typically by using a standalone retrieval tool. However, these processes are problematic: they do not address lesson reuse effectively. We demonstrate how reuse can be facilitated through a representation that highlights reuse conditions (and other features) in the context of lessons learned systems embedded in targeted decision-making processes. We describe a case-based reasoning implementation of this concept for a decision support tool and detail an example

CCBR-Driven Business Process Evolution

Process-aware information systems (PAIS) allow coordinating the execution of business processes by providing the right tasks to the right people at the right time. In order to support a broad spectrum of business processes, PAIS must be flexible at run-time. Ad-hoc deviations from the predefined process schema as well as the quick adaptation of the process schema itself due to changes of the underlying business processes must be supported. This paper presents an integrated approach combining the concepts and methods provided by the process management systems ADEPT and CBRFlow. Integrating these two systems enables ad-hoc modifications of single process instances, the memorization of these modifications using conversational case-based reasoning, and their reuse in similar future situations. In addition, potential process type changes can be derived from cases when similar ad-hoc modifications at the process instance level occur frequently

Active case-based reasoning for lessons delivery systems

Paper presented at The 13th International Florida Artificial Intelligence Research Society Conference, FLAIRS 1999, Menlo Park, FL: pp. 170-174.Exploiting lessons learned is a key knowledge management (KM) task. Currently, most lessons learned systems are passive, stand-alone systems. In contrast, practical KM solutions should be active, interjecting relevant information during decision-making. We introduce an architecture for active lessons delivery systems, an instantiation of it that serves as a monitor, and illustrate it in the context of the conversational case-based plan authoring system HICAP (Muñoz-Avila et al., 1999). When users interact with HICAP, updating its domain objects, this monitor accesses a repository of lessons learned and alerts the user to the ramifications of the most relevant past experiences. We demonstrate this in the context of planning noncombatant evacuation operations

Active delivery for lessons learned systems

Paper presented at The 5th European Workshop on Case-Based Reasoning, EWCBR 2000: pp.322-334.Lessons learned processes, and software systems that support them, have been developed by many organizations (e.g., all USA military branches, NASA, several Department of Energy organizations, the Construction Industry Institute). Their purpose is to promote the dissemination of knowledge gained from the experiences of an organization’s employees. Unfortunately, lessons learned systems are usually ineffective because they invariably introduce new processes when, instead, they should be embedded into the processes that they are meant to improve. We developed an embedded case-based approach for lesson dissemination and reuse that brings lessons to the attention of users rather than requiring them to fetch lessons from a standalone software tool. We demonstrate this active lessons delivery architecture in the context of HICAP, a decision support tool for plan authoring. We also show the potential of active lessons delivery to increase plan quality for a new travel domain

Architecture for planning and execution of missions with fleets of unmanned vehicles

Esta tesis presenta contribuciones en el campo de la planificación automática y la programación de tareas, la rama de la inteligencia artificial que se ocupa de la realización de estrategias o secuencias de acciones típicamente para su ejecución por parte de vehículos no tripulados, robots autónomos y/o agentes inteligentes. Cuando se intenta alcanzar un objetivo determinado, la cooperación puede ser un aspecto clave. La complejidad de algunas tareas requiere la cooperación entre varios agentes. Mas aún, incluso si una tarea es lo suficientemente simple para ser llevada a cabo por un único agente, puede usarse la cooperación para reducir el coste total de la misma. Para realizar tareas complejas que requieren interacción física con el mundo real, los vehículos no tripulados pueden ser usados como agentes. En los últimos años se han creado y utilizado una gran diversidad de plataformas no tripuladas, principalmente vehículos que pueden ser dirigidos sin un humano a bordo, tanto en misiones civiles como militares. En esta tesis se aborda la aplicación de planificación simbólica de redes jerárquicas de tareas (HTN planning, por sus siglas en inglés) en la resolución de problemas de enrutamiento de vehículos (VRP, por sus siglas en inglés) [18], en dominios que implican múltiples vehículos no tripulados de capacidades heterogéneas que deben cooperar para alcanzar una serie de objetivos específicos. La planificación con redes jerárquicas de tareas describe dominios utilizando una descripción que descompone conjuntos de tareas en subconjuntos más pequeños de subtareas gradualmente, hasta obtener tareas del más bajo nivel que no pueden ser descompuestas y se consideran directamente ejecutables. Esta jerarquía es similar al modo en que los humanos razonan sobre los problemas, descomponiéndolos en subproblemas según el contexto, y por lo tanto suelen ser fáciles de comprender y diseñar. Los problemas de enrutamiento de vehículos son una generalización del problema del viajante (TSP, por sus siglas en inglés). La resolución del problema del viajante consiste en encontrar la ruta más corta posible que permite visitar una lista de ciudades, partiendo y acabando en la misma ciudad. Su generalización, el problema de enrutamiento de vehículos, consiste en encontrar el conjunto de rutas de longitud mínima que permite cubrir todas las ciudades con un determinado número de vehículos. Ambos problemas cuentan con una fuerte componente combinatoria para su resolución, especialmente en el caso del VRP, por lo que su presencia en dominios que van a ser tratados con un planificador HTN clásico supone un gran reto. Para la aplicación de un planificador HTN en la resolución de problemas de enrutamiento de vehículos desarrollamos dos métodos. En el primero de ellos presentamos un sistema de optimización de soluciones basado en puntuaciones, que nos permite una nueva forma de conexión entre un software especializado en la resolución del VRP con el planificador HTN. Llamamos a este modo de conexión el método desacoplado, puesto que resolvemos la componente combinatoria del problema de enrutamiento de vehículos mediante un solucionador específico que se comunica con el planificador HTN y le suministra la información necesaria para continuar con la descomposición de tareas. El segundo método consiste en mejorar el planificador HTN utilizado para que sea capaz de resolver el problema de enrutamiento de vehículos de la mejor forma posible sin tener que depender de módulos de software externos. Llamamos a este modo el método acoplado. Con este motivo hemos desarrollado un nuevo planificador HTN que utiliza un algoritmo de búsqueda distinto del que se utiliza normalmente en planificadores de este tipo. Esta tesis presenta nuevas contribuciones en el campo de la planificación con redes jerárquicas de tareas para la resolución de problemas de enrutamiento de vehículos. Se aplica una nueva forma de conexión entre dos planificadores independientes basada en un sistema de cálculo de puntuaciones que les permite colaborar en la optimización de soluciones, y se presenta un nuevo planificador HTN con un algoritmo de búsqueda distinto al comúnmente utilizado. Se muestra la aplicación de estos dos métodos en misiones civiles dentro del entorno de los Proyectos ARCAS y AEROARMS financiados por la Comisión Europea y se presentan extensos resultados de simulación para comprobar la validez de los dos métodos propuestos.This thesis presents contributions in the field of automated planning and scheduling, the branch of artificial intelligence that concerns the realization of strategies or action sequences typically for execution by unmanned vehicles, autonomous robots and/or intelligent agents. When trying to achieve certain goal, cooperation may be a key aspect. The complexity of some tasks requires the cooperation among several agents. Moreover, even if the task is simple enough to be carried out by a single agent, cooperation can be used to decrease the overall cost of the operation. To perform complex tasks that require physical interaction with the real world, unmanned vehicles can be used as agents. In the last years a great variety of unmanned platforms, mainly vehicles that can be driven without a human on board, have been developed and used both in civil and military missions. This thesis deals with the application of Hierarchical Task Network (HTN) planning in the resolution of vehicle routing problems (VRP) [18] in domains involving multiple heterogeneous unmanned vehicles that must cooperate to achieve specific goals. HTN planning describes problem domains using a description that decomposes set of tasks into subsets of smaller tasks and so on, obtaining low-level tasks that cannot be further decomposed and are supposed to be executable. The hierarchy resembles the way the humans reason about problems by decomposing them into sub-problems depending on the context and therefore tend to be easy to understand and design. Vehicle routing problems are a generalization of the travelling salesman problem (TSP). The TSP consists on finding the shortest path that connects all the cities from a list, starting and ending on the same city. The VRP consists on finding the set of minimal routes that cover all cities by using a specific number of vehicles. Both problems have a combinatorial nature, specially the VRP, that makes it very difficult to use a HTN planner in domains where these problems are present. Two approaches to use a HTN planner in domains involving the VRP have been tested. The first approach consists on a score-based optimization system that allows us to apply a new way of connecting a software specialized in the resolution of the VRP with the HTN planner. We call this the decoupled approach, as we tackle the combinatorial nature of the VRP by using a specialized solver that communicates with the HTN planner and provides all the required information to do the task decomposition. The second approach consists on improving and enhancing the HTN planner to be capable of solving the VRP without needing the use of an external software. We call this the coupled approach. For this reason, a new HTN planner that uses a different search algorithm from these commonly used in that type of planners has been developed and is presented in this work. This thesis presents new contributions in the field of hierarchical task network planning for the resolution of vehicle routing problem domains. A new way of connecting two independent planning systems based on a score calculation system that lets them cooperate in the optimization of the solutions is applied, and a new HTN planner that uses a different search algorithm from that usually used in other HTN planners is presented. These two methods are applied in civil missions in the framework of the ARCAS and AEROARMS Projects funded by the European Commission. Extensive simulation results are presented to test the validity of the two approaches

Dynamic theme-based narrative systems

The advent of videogames, and the new forms of expressions they offered, sprouted the possibility of presenting narratives in ways that could capitalize on unique qualities of the media, most notably the agency found in their interactive nature. In spite of many people in the game studies’ field interested in how far said novelty could bring narrative experiences, most approached the creation of narrative systems from a structural approach (especially the classical Aristotelian one), and concurrently, with a bottom-up (characters defining a world) or top-down (world defining characters) perspective. While those more mainstream takes have been greatly progressing what interactive digital narrative can be, this research intended to take a bit of a detour, proposing a functionally similar system that emphasized thematic coherence and responsiveness above all else. Once the theoretical formulation was done, taking into consideration previously similar or tangential systems, a prototype would be developed to make a first step towards validating the proposal, and contribute to building a better understanding of the field’s possibilities

Integration of social values in a multi-agent platform running in a supercomputer

Agent-based modelling is one of the most suitable ways to simulate and analyse complex problems and scenarios, especially those involving social interactions. Multi-agent systems, consisting of multiple agents in a simulation environment, are widely used to understand emergent behaviour in various fields such as sociology, economics and policy. However, existing multi-agent platforms often face challenges in terms of scalability and reasoning capacity. Some platforms can scale well in terms of computation, but lack sophisticated reasoning mechanisms. On the other hand, some platforms employ complex reasoning systems, but this can compromise their scalability. In this work, we have extended an existing platform developed at UPC that enables scalable, parallel HTN planning for complex agents. Our main goal has been to improve the analysis of social relationships between agents by incorporating moral values. Building on previous work done by David Marín on the implementation of the platform, we have made extensions and modifications both formally and in the implementation. We have formalised the additions to the system model and provided an updated implementation. Finally, we have presented a complex example scenario that demonstrates all the additions we have made. This scenario allows us to show how agents' preferences and moral values influence their decisions and actions in a simulated environment. Through this work, we have sought to improve the existing platform and fulfil the spirit and purpose of the platform