An agent-based simulator to estimate domestic energy use

Throughout recent years, there has been an increase in the population size, as well as a fast economic growth, which has led to an increase of the energy demand that comes mainly from fossil fuels. In order to reduce the ecological footprint, governments have implemented sustainable measures and it is expected that by 2035 the energy produced from renewable energy sources, such as wind and solar would be responsible for one-third of the energy produced globally. However, since the energy produced from renewable sources is governed by the availability of the respective primary energy source there is often a mismatch between production and demand, which could be solved by adding flexibility on the demand side through demand response (DR). DR programs influence the end-user electricity usage by changing its cost along the time. Under this scenario the user needs to estimate the energy demand and on-site production in advance to plan its energy demand according to the energy price. This work focuses on the development of an agent-based electrical simulator, capable of: (a) estimating the energy demand and on-site generation with a 1-min time resolution for a 24-h period, (b) calculating the energy price for a given scenario, (c) making suggestions on how to maximize the usage of renewable energy produced on-site and to lower the electricity costs by rescheduling the use of certain appliances. The results show that this simulator allows reducing the energy bill by 11% and almost doubling the use of renewable energy produced on-site.

Adaptive intelligent agent approach to guide the web navigation on the PLAN-G distance learning platform

Hypermedia systems based on the Web for open distance education are becoming increasinglypopular as tools for user-driven access learning information. Adaptive hypermedia is a new direction in research within the area of user-adaptive systems, to increase its functionality by making it personalized [Eklu 961. This paper sketches a general agents architecture to include navigationaladaptability and user-friendly processes which would guide and accompany the student during hislher learning on the PLAN-G hypermedia system (New Generation Telematics Platform to Support Open and Distance Learning), with the aid of computer networks and specifically WWW technology [Marz 98-1] [Marz 98-2]. The PLAN-G actual prototype is successfully used with some informatics courses (the current version has no agents yet). The propased multi-agent system, contains two different types of adaptive autonomous software agents: Personal Digital Agents {Interface), to interacl directly with the student when necessary; and Information Agents (Intermediaries), to filtrate and discover information to learn and to adapt navigation space to a specific student

Flocking behaviour: Agent-based simulation and hierarchical leadership.

We have studied how leaders emerge in a group as a consequence of interactions among its members. We propose that leaders can emerge as a consequence of a self-organized process based on local rules of dyadic interactions among individuals. Flocks are an example of self-organized behaviour in a group and properties similar to those observed in flocks might also explain some of the dynamics and organization of human groups. We developed an agent-based model that generated flocks in a virtual world and implemented it in a multi-agent simulation computer program that computed indices at each time step of the simulation to quantify the degree to which a group moved in a coordinated way (index of flocking behaviour) and the degree to which specific individuals led the group (index of hierarchical leadership). We ran several series of simulations in order to test our model and determine how these indices behaved under specific agent and world conditions. We identified the agent, world property, and model parameters that made stable, compact flocks emerge, and explored possible environmental properties that predicted the probability of becoming a leader.

Une approche multi-agents pour le développement d'un jeu vidéo

Un système multi-agents est composé de plusieurs agents autonomes qui interagissent entre eux dans un environnement commun. Ce mémoire vise à démontrer l’utilisation d’un système multi-agents pour le développement d’un jeu vidéo. Tout d’abord, une justification du choix des concepts d’intelligence artificielle choisie est exposée. Par la suite, une approche pratique est utilisée en effectuant le développement d’un jeu vidéo. Pour ce faire, le jeu fut développé à partir d’un jeu vidéo mono-agent existant et mo- difié en système multi-agents afin de bien mettre en valeur les avantages d’un système multi-agents dans un jeu vidéo. Le développement de ce jeu a aussi démontré l’applica- tion d’autres concepts en intelligence artificielle comme la recherche de chemins et les arbres de décisions. Le jeu développé pour ce mémoire viens appuyer les conclusions des différentes recherches démontrant que l’utilisation d’un système multi-agents per- met de réaliser un comportement plus réaliste pour les joueurs non humains et bien plus compétitifs pour le joueur humain.

Agent Based Evacuation Simulation Using Leader-Follower Model

Agent based simulation is a widely developing area in artificial intelligence.The simulation studies are extensively used in different areas of disaster management. This work deals with the study of an agent based evacuation simulation which is being done to handle the various evacuation behaviors.Various emergent behaviors of agents are addressed here. Dynamic grouping behaviors of agents are studied. Collision detection and obstacle avoidances are also incorporated in this approach.Evacuation is studied with single exits and multiple exits and efficiency is measured in terms of evacuation rate, collision rate etc.Net logo is the tool used which helps in the efficient modeling of scenarios in evacuation

Offline Emergence Engineering For Agent Societies

Many examples for emergent behaviors may be observed in self-organizing physical and biological systems which prove to be robust, stable, and adaptable. Such behaviors are often based on very simple mechanisms and rules, but artificially creating them is a challenging task which does not comply with traditional software engineering. In this article, we propose a hybrid approach by combining strategies from Genetic Programming and agent software engineering, and demonstrate that this approach effectively yields an emergent design for given problems.

Adaptive intelligent agent approach to guide the web navigation on the PLAN-G distance learning platform

Hypermedia systems based on the Web for open distance education are becoming increasingly popular as tools for user-driven access learning information. Adaptive hypermedia is a new direction in research within the area of user-adaptive systems, to increase its functionality by making it personalized [Eklu 961. This paper sketches a general agents architecture to include navigational adaptability and user-friendly processes which would guide and accompany the student during hislher learning on the PLAN-G hypermedia system (New Generation Telematics Platform to Support Open and Distance Learning), with the aid of computer networks and specifically WWW technology [Marz 98-1] [Marz 98-2]. The PLAN-G actual prototype is successfully used with some informatics courses (the current version has no agents yet). The propased multi-agent system, contains two different types of adaptive autonomous software agents: Personal Digital Agents {Interface), to interacl directly with the student when necessary; and Information Agents (Intermediaries), to filtrate and discover information to learn and to adapt navigation space to a specific student

Introspection on control-grounded capabilities. An agent inspired approach for control

Introspecció sobre la dinàmica dels agents té un important impacte en decisions individuals i cooperatives en entorns multi-agent. Introspecció, una habilitat cognitiva provinent de la metàfora "agent", permet que els agents siguin conscients de les seves capacitats per a realitzar correctament les tasques. Aquesta introspecció, principalment sobre capacitats relacionades amb la dinàmica, proporciona als agents un raonament adequat per a assolir compromisos segurs en sistemes cooperatius. Per a tal fi, les capacitats garanteixen una representació adequada i explícita de tal dinàmica. Aquest enfocament canvia i millora la manera com els agents poden coordinar-se per a portar a terme tasques i com gestionar les seves interaccions i compromisos en entorns cooperatius. L'enfocament s'ha comprovat en escenaris on la coordinació és important, beneficiosa i necessària. Els resultats i les conclusions són presentats ressaltant els avantatges de la introspecció en la millora del rendiment dels sistemes multi-agent en tasques coordinades i assignació de tasques.

Studies of dynamics of physical agent ecosystems

This thesis addresses the problem of learning in physical heterogeneous multi-agent systems (MAS) and the analysis of the benefits of using heterogeneous MAS with respect to homogeneous ones. An algorithm is developed for this task; building on a previous work on stability in distributed systems by Tad Hogg and Bernardo Huberman, and combining two phenomena observed in natural systems, task partition and hierarchical dominance. This algorithm is devised for allowing agents to learn which are the best tasks to perform on the basis of each agent's skills and the contribution to the team global performance. Agents learn by interacting with the environment and other teammates, and get rewards from the result of the actions they perform. This algorithm is specially designed for problems where all robots have to co-operate and work simultaneously towards the same goal. One example of such a problem is role distribution in a team of heterogeneous robots that form a soccer team, where all members take decisions and co-operate simultaneously. Soccer offers the possibility of conducting research in MAS, where co-operation plays a very important role in a dynamical and changing environment. For these reasons and the experience of the University of Girona in this domain, soccer has been selected as the test-bed for this research. In the case of soccer, tasks are grouped by means of roles. One of the most interesting features of this algorithm is that it endows MAS with a high adaptability to changes in the environment. It allows the team to perform their tasks, while adapting to the environment. This is studied in several cases, for changes in the environment and in the robot's body. Other features are also analysed, especially a parameter that defines the fitness (biological concept) of each agent in the system, which contributes to performance and team adaptability. The algorithm is applied later to allow agents to learn in teams of homogeneous and heterogeneous robots which roles they have to select, in order to maximise team performance. The teams are compared and the performance is evaluated in the games against three hand-coded teams and against the different homogeneous and heterogeneous teams built in this thesis. This section focuses on the analysis of performance and task partition, in order to study the benefits of heterogeneity in physical MAS. In order to study heterogeneity from a rigorous point of view, a diversity measure is developed building on the hierarchic social entropy defined by Tucker Balch. This is adapted to quantify physical diversity in robot teams. This tool presents very interesting features, as it can be used in the future to design heterogeneous teams on the basis of the knowledge on other teams.

A logic-based agent that plans for extended reachability goals

Planning to reach a goal is an essential capability for rational agents. In general, a goal specifies a condition to be achieved at the end of the plan execution. In this article, we introduce nondeterministic planning for extended reachability goals (i.e., goals that also specify a condition to be preserved during the plan execution). We show that, when this kind of goal is considered, the temporal logic CTL turns out to be inadequate to formalize plan synthesis and plan validation algorithms. This is mainly due to the fact that the CTL`s semantics cannot discern among the various actions that produce state transitions. To overcome this limitation, we propose a new temporal logic called alpha-CTL. Then, based on this new logic, we implement a planner capable of synthesizing reliable plans for extended reachability goals, as a side effect of model checking.

Agent-Oriented Data Curation in Bioinformatics

The practitioners of bioinformatics require increasing sophistication from their software tools to take into account the particular characteristics that make their domain complex. For example, there is a great variation of experience of researchers, from novices who would like guidance from experts in the best resources to use to experts that wish to take greater management control of the tools used in their experiments. Also, the range of available, and conflicting, data formats is growing and there is a desire to automate the many trivial manual stages of in-silico experiments. Agent-oriented software development is one approach to tackling the design of complex applications. In this paper, we argue that, in fact, agent-oriented development is a particularly well-suited approach to developing bioinformatics tools that take into account the wider domain characteristics. To illustrate this, we design a data curation tool, which manages the format of experimental data, extend it to better account for the extra requirements placed by the domain characteristics, and show how the characteristics lead to a system well suited to an agent-oriented view.