Simplifying the development of intelligent agents

Intelligent agents is a powerful Artificial Intelligence technology which shows considerable promise as a new paradigm for mainstream software development. However, despite their promise, intelligent agents are still scarce in the market place. A key reason for this is that developing intelligent agent software requires significant training and skill: a typical developer or undergraduate struggles to develop good agent systems using the Belief Desire Intention (BDI) model (or similar models). This paper identifies the concept set which we have found to be important in developing intelligent agent systems and the relationships between these concepts. This concept set was developed with the intention of being clearer, simpler, and easier to use than current approaches.We also describe briefly a (very simplified) example from one of the projects we have worked on (RoboRescue), illustrating the way in which these concepts are important in designing and developing intelligent software agents

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks

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

AGENT-BASED UNDER HOOD PACKING

Improving vehicle performance and passenger comfort has been a prime engineering concern and focus of research for many years in automotive design. Turning to high-performance components in an effort to improve vehicle performance alone is often not enough and their placement and interactions with other components should also be an integral part of the improvement process. With the advancement in hybrid electric vehicle technology, the packing of components under the hood is ever more essential and challenging. Under hood packing is a multi-objective optimization problem with many, and mostly conflicting objectives. A non-deterministic multi-objective evolutionary algorithm needs to be integrated with the packing algorithm to obtain solutions. However, it is almost impossible to find optimal solutions in a limited amount of time due to the computationally intensive algorithm. Therefore, a new and efficient approach needs to be developed. This study applies an agent-based approach to the under hood vehicle packing problem with three objectives, namely: center of gravity, survivability, and maintainability subject to no overlap among components and with the enclosure, and minimum ground clearance. As per the weak notion of agency, a layered architecture is built with an agent on top of object model. A non-deterministic evolutionary multi-objective algorithm (AMGA-2) is used to identify non-dominated solutions, speed up the convergence to a non-dominated set and prevents unpredictability in the agent system. The developed agent-based model is applied to a passenger car but, it can also address large packing problems for SUVs and Trucks (FMTV). This work demonstrates the applicability and benefits of an agent-based approach to the packing problem

Multi Agent Systems in Logistics: A Literature and State-of-the-art Review

Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?†Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution.supply chain;MAS;multi agent systems

The next step after Japan? (Virtual reality, training and crisis management). Transatlantic Security Paper No. 2, June 2012

The recent crisis in Japan, which combined tsunami and technological events, shows that any crisis, especially those in developed and developing countries, is from here out a hybrid crisis, mixing natural factors and human/technological (NATECH). Faced with such dramatic events, which exceed any means available for emergency rescue service, it is necessary a) to remain prudent and b) to prepare. One of the means for preparing is unquestionably training. However, here, undoubtedly there are important constraints: How to train, for example, while reproducing vividly and realistically, an event? How to exceed the admittedly useful, although very limited, level of the table-top exercise? How also to avoid the unnecessary mobilization of dozens, even hundreds, of field and operation staffers to take part in an exercise which could lead to a disappointing outcome? A major crisis, a major exercise, in effect. The solution of virtual reality has emerged, in Europe and in the United States. It is also sometimes called “serious game”

Abductive Design of BDI Agent-based Digital Twins of Organizations

For a Digital Twin - a precise, virtual representation of a physical counterpart - of a human-like system to be faithful and complete, it must appeal to a notion of anthropomorphism (i.e., attributing human behaviour to non-human entities) to imitate (1) the externally visible behaviour and (2) the internal workings of that system. Although the Belief-Desire-Intention (BDI) paradigm was not developed for this purpose, it has been used successfully in human modeling applications. In this sense, we introduce in this thesis the notion of abductive design of BDI agent-based Digital Twins of organizations, which builds on two powerful reasoning disciplines: reverse engineering (to recreate the visible behaviour of the target system) and goal-driven eXplainable Artificial Intelligence (XAI) (for viewing the behaviour of the target system through the lens of BDI agents). Precisely speaking, the overall problem we are trying to address in this thesis is to “Find a BDI agent program that best explains (in the sense of formal abduction) the behaviour of a target system based on its past experiences . To do so, we propose three goal-driven XAI techniques: (1) abductive design of BDI agents, (2) leveraging imperfect explanations and (3) mining belief-based explanations. The resulting approach suggests that using goal-driven XAI to generate Digital Twins of organizations in the form of BDI agents can be effective, even in a setting with limited information about the target system’s behaviour

COMBINED ARTIFICIAL INTELLIGENCE BEHAVIOUR SYSTEMS IN SERIOUS GAMING

This thesis proposes a novel methodology for creating Artificial Agents with semi-realistic behaviour, with such behaviour defined as overcoming common limitations of mainstream behaviour systems; rapidly switching between actions, ignoring “obvious” event priorities, etc. Behaviour in these Agents is not fully realistic as some limitations remain; Agents have a “perfect” knowledge about the surrounding environment, and an inability to transfer knowledge to other Agents (no communication). The novel methodology is achieved by hybridising existing Artificial Intelligence (AI) behaviour systems. In most artificial agents (Agents) behaviour is created using a single behaviour system, whereas this work combines several systems in a novel way to overcome the limitations of each. A further proposal is the separation of behavioural concerns into behaviour systems that are best suited to their needs, as well as describing a biologically inspired memory system that further aids in the production of semi-realistic behaviour. Current behaviour systems are often inherently limited, and in this work it is shown that by combining systems that are complementary to each other, these limitations can be overcome without the need for a workaround. This work examines in detail Belief Desire Intention systems, as well as Finite State Machines and explores how these methodologies can complement each other when combined appropriately. By combining these systems together a hybrid system is proposed that is both fast to react and simple to maintain by separating behaviours into fast-reaction (instinctual) and slow-reaction (behavioural) behaviours, and assigning these to the most appropriate system. Computational intelligence learning techniques such as Artificial Neural Networks have been intentionally avoided, as these techniques commonly present their data in a “black box” system, whereas this work aims to make knowledge explicitly available to the user. A biologically inspired memory system has further been proposed in order to generate additional behaviours in Artificial Agents, such as behaviour related to forgetfulness. This work explores how humans can quickly recall information while still being able to store millions of pieces of information, and how this can be achieved in an artificial system