Reasoning about Goal-Plan Trees in Autonomous Agents: Development of Petri net and Constraint-Based Approaches with Resulting Performance Comparisons

Multi-agent systems and autonomous agents are becoming increasingly important in current computing technology. In many applications, the agents are often asked to achieve multiple goals individually or within teams where the distribution of these goals may be negotiated among the agents. It is expected that agents should be capable of working towards achieving all its currently adopted goals concurrently. However, in doing so, the goals can interact both constructively and destructively with each other, so a rational agent must be able to reason about these interactions and any other constraints that may be imposed on them, such as the limited availability of resources that could affect their ability to achieve all adopted goals when pursuing them concurrently. Currently, agent development languages require the developer to manually identify and handle these circumstances. In this thesis, we develop two approaches for reasoning about the interactions between the goals of an individual agent. The first of these employs Petri nets to represent and reason about the goals, while the second uses constraint satisfaction techniques to find efficient ways of achieving the goals. Three types of reasoning are incorporated into these models: reasoning about consumable resources where the availability of the resources is limited; the constructive interaction of goals whereby a single plan can be used to achieve multiple goals; and the interleaving of steps for achieving different goals that could cause one or more goals to fail. Experimental evaluation of the two approaches under various different circumstances highlights the benefits of the reasoning developed here whilst also identifying areas where one approach provides better results than the other. This can then be applied to suggest the underlying technique used to implement the reasoning that the agent may want to employ based on the goals it has been assigned

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

El modelado basado en agentes es una de las formas más apropiadas para simular y analizar problemas y simulaciones complejas, como la simulación de entornos y escenarios sociales. El tipo de plataforma que más se utiliza en estas tareas es la de un sistema multiagente. Los sistemas multiagente se componen de varios actores (agentes) en un entorno de simulación concreto, y cada uno de ellos posee un conocimiento y un comportamiento individual. Estos sistemas pueden utilizarse para analizar el comportamiento emergente colectivo en contextos como la sociología, la economía, la elaboración de políticas sociales y económicas, etc. Las plataformas multiagente actuales o bien escalan bastante bien en computación pero implementan mecanismos de razonamiento muy simples, o bien emplean sistemas de razonamiento complejos a costa de escalabilidad. En un trabajo reciente realizado en la UPC, se ha propuesto, teorizado e implementado una plataforma que permite escalar y ejecutar paralelamente agentes complejos con planificación HTN. Este proyecto amplía dicha plataforma para permitir un mejor análisis de las relaciones sociales entre los agentes mediante las preferencias sobre sus objetivos, las preferencias sobre sus planes, sus acciones y valores morales, a la vez que nos aseguramos de que nuestras adiciones sean escalables, para mantener el espíritu y el propósito de la plataforma. En este trabajo, partimos del trabajo previo realizado por Dmitry Gnatyshak sobre la implementación de dicha plataforma, y lo ampliamos, tanto formalmente como a nivel de implementación. Formalizamos las ampliaciones del modelo del sistema, así como sus modificaciones, y hacemos lo mismo con la implementación. Al final, proporcionamos un complejo escenario de ejemplo para mostrar todas las ampliaciones que hemos creado y/o añadido.Agent-based modeling is one of the most suitable ways to simulate and analyze complex problems and simulations, such as the simulation of societal environments and scenarios. The kind of platform most commonly used in these endeavors is that of a multi-agent system. Multi-agent systems are comprised of various actors (agents) in a concrete simulation environment, each of them possessing an individual knowledge and an individual behavior. These systems can be used to analyze collective emergent behavior in contexts such as sociology, economics, policy making, etc. Current Multi-agent platforms either scale in computation quite well but implement very simple reasoning mechanisms, or employ complex reasoning systems at the expense of scalability. In recent work done at UPC, a platform enabling complex agents with HTN planning to scale and run parallelly was proposed, theorized, and implemented. This project extends said platform to enable a better analysis of the social relationships between agents by means of preferences over their objectives, preferences over their plans, actions, and moral values, while making sure our additions are scalable, to maintain the spirit and purpose of the platform. In this work, we start from the previous work done by Dmitry Gnatyshak on implementing said platform, and we expand it, both formally and imple- mentation-wise. We formalize the additions to the model of the system, as well as its modifications, and we do the same for the implementation. In the end, we provide a complex example scenario to showcase all the additions we have created

Proceedings of The Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW 2010)

http://ceur-ws.org/Vol-627/allproceedings.pdfInternational audienceMALLOW-2010 is a third edition of a series initiated in 2007 in Durham, and pursued in 2009 in Turin. The objective, as initially stated, is to "provide a venue where: the cost of participation was minimum; participants were able to attend various workshops, so fostering collaboration and cross-fertilization; there was a friendly atmosphere and plenty of time for networking, by maximizing the time participants spent together"