24,294 research outputs found
Multi-agent simulations for emergency situations in an airport scenario
This paper presents a multi-agent framework using Net- Logo to simulate humanand collective behaviors during emergency evacuations. Emergency situationappears when an unexpected event occurs. In indoor emergency situation, evacuation plans defined by facility manager explain procedure and safety ways tofollow in an emergency situation. A critical and public scenario is an airportwhere there is an everyday transit of thousands of people. In this scenario theimportance is related with incidents statistics regarding overcrowding andcrushing in public buildings. Simulation has the objective of evaluating buildinglayouts considering several possible configurations. Agents could be based onreactive behavior like avoid danger or follow other agent, or in deliberative behaviorbased on BDI model. This tool provides decision support in a real emergencyscenario like an airport, analyzing alternative solutions to the evacuationprocess.Publicad
A multi-agent system with application in project scheduling
The new economic and social dynamics increase project complexity and makes scheduling problems more difficult, therefore scheduling requires more versatile solutions as Multi Agent Systems (MAS). In this paper the authors analyze the implementation of a Multi-Agent System (MAS) considering two scheduling problems: TCPSP (Time-Constrained Project Scheduling), and RCPSP (Resource-Constrained Project Scheduling). The authors propose an improved BDI (Beliefs, Desires, and Intentions) model and present the first the MAS implementation results in JADE platform.multi-agent architecture, scheduling, project management, BDI architecture, JADE.
Joint Optimal Pricing and Electrical Efficiency Enforcement for Rational Agents in Micro Grids
In electrical distribution grids, the constantly increasing number of power
generation devices based on renewables demands a transition from a centralized
to a distributed generation paradigm. In fact, power injection from Distributed
Energy Resources (DERs) can be selectively controlled to achieve other
objectives beyond supporting loads, such as the minimization of the power
losses along the distribution lines and the subsequent increase of the grid
hosting capacity. However, these technical achievements are only possible if
alongside electrical optimization schemes, a suitable market model is set up to
promote cooperation from the end users. In contrast with the existing
literature, where energy trading and electrical optimization of the grid are
often treated separately or the trading strategy is tailored to a specific
electrical optimization objective, in this work we consider their joint
optimization. Specifically, we present a multi-objective optimization problem
accounting for energy trading, where: 1) DERs try to maximize their profit,
resulting from selling their surplus energy, 2) the loads try to minimize their
expense, and 3) the main power supplier aims at maximizing the electrical grid
efficiency through a suitable discount policy. This optimization problem is
proved to be non convex, and an equivalent convex formulation is derived.
Centralized solutions are discussed first, and are subsequently distributed.
Numerical results to demonstrate the effectiveness of the so obtained optimal
policies are then presented
Rational Verification in Iterated Electric Boolean Games
Electric boolean games are compact representations of games where the players
have qualitative objectives described by LTL formulae and have limited
resources. We study the complexity of several decision problems related to the
analysis of rationality in electric boolean games with LTL objectives. In
particular, we report that the problem of deciding whether a profile is a Nash
equilibrium in an iterated electric boolean game is no harder than in iterated
boolean games without resource bounds. We show that it is a PSPACE-complete
problem. As a corollary, we obtain that both rational elimination and rational
construction of Nash equilibria by a supervising authority are PSPACE-complete
problems.Comment: In Proceedings SR 2016, arXiv:1607.0269
Aspects of cooperating agents
An overview on aspects about cooperating agents is presented. As multiagent systems are various, we start with a classification of multiagent systems which is particularly influenced by an article from Decker, Durfee, and Lesser [Decker& 89]. In the following, the aspects of communication, planning, and negotiation are examined. On the occasion of communication, the discussion is split into: no communication - simple protocols - artificial languages. The planning aspect is broken into sections: from classical to multiagent planning - a general multiagent planning theory - intention - intention-directed multiagent planning. Finally, a summary of Brigitte and Hassan LĂąasri and Victor Lesser\u27s negotiation theory will be presented
Evolutionary Tournament-Based Comparison of Learning and Non-Learning Algorithms for Iterated Games
Evolutionary tournaments have been used effectively as a tool for comparing game-playing algorithms. For instance, in the late 1970's, Axelrod organized tournaments to compare algorithms for playing the iterated prisoner's dilemma (PD) game. These tournaments capture the dynamics in a population of agents that periodically adopt relatively successful algorithms in the environment. While these tournaments have provided us with a better understanding of the relative merits of algorithms for iterated PD, our understanding is less clear about algorithms for playing iterated versions of arbitrary single-stage games in an environment of heterogeneous agents. While the Nash equilibrium solution concept has been used to recommend using Nash equilibrium strategies for rational players playing general-sum games, learning algorithms like fictitious play may be preferred for playing against sub-rational players. In this paper, we study the relative performance of learning and non-learning algorithms in an evolutionary tournament where agents periodically adopt relatively successful algorithms in the population. The tournament is played over a testbed composed of all possible structurally distinct 2Ă2 conflicted games with ordinal payoffs: a baseline, neutral testbed for comparing algorithms. Before analyzing results from the evolutionary tournament, we discuss the testbed, our choice of representative learning and non-learning algorithms and relative rankings of these algorithms in a round-robin competition. The results from the tournament highlight the advantage of learning algorithms over players using static equilibrium strategies for repeated plays of arbitrary single-stage games. The results are likely to be of more benefit compared to work on static analysis of equilibrium strategies for choosing decision procedures for open, adapting agent society consisting of a variety of competitors.Repeated Games, Evolution, Simulation
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