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

Uncertain information combination for decision making in smart grid BDI agent systems

In a smart grid SCADA (supervisory control and data acquisition) system, sensor information (e.g. temperature, voltage, frequency, etc.) from heterogeneous sources can be used to reason about the true system state (e.g. faults, attacks, etc.). Before this is possible, it is necessary to combine information in a consistent way. However, information may be uncertain or incomplete while the sensors may be unreliable or conflicting. To address these issues, we apply Dempster-Shafer (DS) theory to model the information from each source as a mass function. Each mass function is then discounted to reflect the reliability of the source. Finally, relevant mass functions (after evidence propagation) are combined using a context-dependent combination rule to produce a single combined mass function used for reasoning. We model a smart grid SCADA system in the belief-desire-intention (BDI) multi-agent framework to demonstrate how our approach can be used to handle the combined uncertain sensor information. In particular, the combined mass function is transformed into a probability distribution for decision-making. Based on this result, the agent can determine which state is most plausible and insert a corresponding AgentSpeak belief atom into its belief base. These beliefs about the environment affect the selection of predefined plans, which in turn determine how the agent will behave. We also identify conditions when a combination should occur to ensure the reactiveness of the agent

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

Robust execution of BDI agent programs by exploiting synergies between intentions

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

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

A data-driven approach towards a realistic and generic crowd simulation framework

Jacob Sinclair studied and developed a data-driven approach towards a realistic and generic crowd simulation framework. He found that by using virtual reality and questionnaires, we can gather all types of real world data. He also found that an AI framework developed using all types of data can produce similar results to the real world. This AI framework has the potential to be used to improve areas such as emergency management and response, traffic control, building design, video games, etc

Action-level intention selection for BDI agents

Belief-Desire-Intention agents typically pursue multiple goals in parallel. However the interleaving of steps in different intentions may result in conflicts, e.g., where the execution of a step in one plan makes the execution of a step in another concurrently executing plan impossible. Previous approaches to avoiding conflicts between concurrently executing intentions treat plans as atomic units, and attempt to interleave plans in different intentions so as to minimise conflicts. However some conflicts cannot be resolved by appropriate ordering of plans and can only be resolved by appropriate interleaving of steps within plans. In this paper, we present SA, an approach to intention selection based on Single-Player Monte Carlo Tree Search that selects which intention to progress at the current cycle at the level of individual plan steps. We evaluate the performance of our approach in a range of scenarios of increasing difficulty in both static and dynamic environments. The results suggest SA out-performs existing approaches to intention selection both in terms of goals achieved and the variance in goal achievement time