Paulo Trigo

The electricity market as a multi-agent system

This paper proposes a multi-agent based simulation (MABS) framework to construct an artificial electric power market where heterogeneous, rationally bounded and learning agents co-evolve dynamically. The proposed framework aims to facilitate the integration of two MABS constructs: (i) the design of the environmental physical properties and entities, and (ii) the simulation models of resources and both the decision-making (deliberative) and reactive agents. The framework is materialized in an experimental setup involving distinct power generator companies which operate in the market and search for the trading strategies that best exploit their generating unitspsila resources. The experimental results show a coherent market behavior that emerges from the overall simulated environment.

Decisions with multiple simultaneous goals and uncertain causal effects

A key aspect of decision-making in a disaster response scenario is the capability to evaluate multiple and simultaneously perceived goals. Current competing approaches to build decision-making agents are either mental-state based as BDI, or founded on decision-theoretic models as MDP. The BDI chooses heuristically among several goals and the MDP searches for a policy to achieve a specific goal. In this paper we develop a preferences model to decide among multiple simultaneous goals. We propose a pattern, which follows a decision-theoretic approach, to evaluate the expected causal effects of the observable and non-observable aspects that inform each decision. We focus on yes-or-no (i.e., pursue or ignore a goal) decisions and illustrate the proposal using the RoboCupRescue simulation environment.

TEMMAS: The Electricity Market Multi-Agent Simulator

This paper presents the multi-agent TEMMAS simulator of an artificial electric power market populated with learning agents. The simulator facilitates the integration of two modeling constructs: i) the specification of the environmental physical market properties, and ii) the modeling of the decision-making (deliberative) and reactive agents.

The multi-team formation precursor of teamwork

We formulate the multi-team formation (M-TF) domain-independent problem and describe a generic solution for the problem. We illustrate the M-TF preference relation component in the domain of a large-scale disaster response simulation environment. The M-TF problem is the precursor of teamwork that explicitly addresses the achievement of several short time period goals, where the work to achieve the complete set of goals overwhelms the working capacity of the team formation space (all teams formed from the finite set of available agents). Decisions regarding team formation are made by the agents considering their own probabilistic beliefs and utility preferences about the whole (known) set of goals to achieve. The RoboCupRescue simulated large-scale disaster domain is used to illustrate the design of the preference relation domain-specific M-TF component.

Coordination with collective and individual decisions

The response to a large-scale disaster, e.g. an earthquake or a terrorist incident, urges for low-cost policies that coordinate sequential decisions of multiple agents. Decisions range from collective (common good) to individual (self-interested) perspectives, intuitively shaping a two-layer decision model. However, current decision theoretic models are either purely collective or purely individual and seek optimal policies. We present a two-layer, collective versus individual (CvI) decision model and explore the tradeoff between cost reduction and loss of optimality while learning coordination skills. Experiments, in a partially observable domain, test our approach for learning a collective policy and results show near-optimal policies that exhibit coordinated behavior.

Collective-Intelligence and Decision-Making

Decision-making, while performed by humans, is also expected to be found in most (artificial) intelligent systems. Usually, the cognitive research assumption is that the individual is the correct unit for the analysis of (human) intelligence. Yet, the multi-agent assumption is that of a society of interacting individuals (agency) that collectively supersedes individual capabilities. Therefore, the entire society of agents is, itself, an additional locus for the analysis of this collective- intelligence. In this paper we propose models that explore the agent-agency mutual influence from the decision-making perspective. We outline three case study scenarios for the models’ experimental evaluation: i) large-scale disasters, ii) electricity markets, and iii) Web-empowered knowledge and social connectivity. The scenario-driven evaluations are being used to guide our research efforts.

Virtual) Agents for running electricity markets

Abstract This paper describes a multi-agent based simulation (MABS) framework to construct an artificial electric power market populated with learning agents. The artificial market, named TEMMAS (The Electricity Market Multi-Agent Simulator), explores the integration of two design constructs: (i) the specification of the environmental physical market properties and (ii) the specification of the decision-making (deliberative) and reactive agents. TEMMAS is materialized in an experimental setup involving distinct power generator companies that operate in the market and search for the trading strategies that best exploit their generating units’ resources. The experimental results show a coherent market behavior that emerges from the overall simulated environment.

Simulation of Electrical Distributed Energy Resources for Electrical Vehicles Charging Process Strategy

This paper presents a simulation platform for control and monitor the Electric Vehicle charging process, based on existing power distribution limitations and Micro generation capacity. The goal of this research is to simulate the energy consumption and their unexpected behavior, using past experience and taking into account distribution network and home power limitation to find an intelligent charging pattern. This paper proposes a novel approach for this problem based on a simulation platform where stochastic process is adopted to perform unexpected user behavior. This simulation platform can be used to determine the capability of the actual electrical distribution network for supplying energy to the final consumers and for charging the bank of batteries of electrical vehicles, which can occur simultaneously.

On the Operationality of Moral-Sense Decision Making

Looking to the operation of an agent architecture, ie. its goal generation and maintenance processing, is relevant to understand fully how a moral based agent takes appropriate and diverse decisions within social situations of serious games. How decision does happen is a complex issue and the major motivation of this paper, and our answer, the proposal of a new architecture, is supported on the clarification of the organization and structure of an agent, ie. the interpretation of agent actions (moral-driven behaviour) under the pressure of severe constraints.

Comparison of Distributed Computing Approaches to Complexity of n-gram Extraction

In this paper we compare different technologies that support distributed computing as a means to address complex tasks. We address the task of n-gram text extraction which is a big computational given a large amount of textual data to process. In order to deal with such complexity we have to adopt and implement parallelization patterns. Nowadays there are several patterns, platforms and even languages that can be used for the parallelization task. We implemented this task on three platforms: (1) MPJ Express, (2) Apache Hadoop, and (3) Apache Spark. The experiments were implemented using two kinds of datasets composed by: (A) a large number of small files, and (B) a small number of large files. Each experiment uses both datasets and the experiment repeats for a set of different file sizes. We compared performance and efficiency among MPJ Express, Apache Hadoop and Apache Spark. As a final result we are able to provide guidelines for choosing the platform that is best suited for each kind of data set regarding its overall size and granularity of the input data.