In recent years, there have been many efforts for distributing a big and complex duty among some agents in order to do it more simply. One of the theories that has received attention recently is called Embodiment. Acc...
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ISBN:
(纸本)9781479919710
In recent years, there have been many efforts for distributing a big and complex duty among some agents in order to do it more simply. One of the theories that has received attention recently is called Embodiment. According to this theory, in cooperation of a set of agents for performing a particular task, expertise might not be integrated in a centralized controller;rather it gradually spreads to component and agents. Based on this theory and despite the fact that the human brain is the main controller, expertise is integrated in the body organs gradually. In multi-agent systems a central controller controls and programs whole duties and jobs that agents should perform, this leads to have a very complicated central controller. While in embodiment systems, initially each agent with different structures are fed with some knowledge in the specific duty and then are placed together to accomplish a common task. Now, we will examine this issue that how much improvement is earned when every agent has got a specific amount of partial knowledge separately in comparison with whole structure is defined beforehand. We try to implement embodied system on legs of a quadruped and compare result with the multi-agent system.
Crowd movement during natural disasters and major accidents can affect the success of evacuation procedure. Thus to develop an effective evacuation plan, a wide-range of scenarios causing different routes and patterns...
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ISBN:
(纸本)9781467381437
Crowd movement during natural disasters and major accidents can affect the success of evacuation procedure. Thus to develop an effective evacuation plan, a wide-range of scenarios causing different routes and patterns of crowd movement should be considered. Recent researches in agent-based simulation have achieved techniques to simulate crowd movement in emergency scenarios at city scale. However simulating crowd movement at a macroscopic level for disasters which might affect several cities, like large-scale flood, is still a challenge. This paper addresses this issue and makes three contributions. First, the development of an agent-based layered model to simulate large-scale flood using GIS is demonstrated. Second, the simulation of crowd agents' movement on available roads is presented. Third, the preliminary experiments running on private Cloud server is reported. The experiments cover case studies illustrated the movement of crowd agents around Thailand while several parts of the country were inundated. The 2D animation depict the movement of crowd;and the simulation results show the status of the agents and the amount of individuals which required shelters. To simulate one day events, the simulator took 4-9 hours execution time depending on the severity of floods and available facilities.
Negotiations among autonomous agents have gained a mass of attention from a variety of communities in the past decade. This paper deals with a prominent type of automated negotiations, namely, multilateral multi-issue...
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ISBN:
(纸本)9781509001644
Negotiations among autonomous agents have gained a mass of attention from a variety of communities in the past decade. This paper deals with a prominent type of automated negotiations, namely, multilateral multi-issue negotiation that runs under real-time constraints and in which the negotiating agents have no prior knowledge about their opponents' preferences over the space of negotiation outcomes. We propose a novel negotiation approach which enables an agent to reach an efficient agreement with multiple opponents. The proposed approach achieves that goal by, 1) employing sparse pseudo-input Gaussian processes to model the behavior of opponents, 2) learning fuzzy opponent preferences to increase the satisfaction of other parties, and 3) adopting an adaptive decision-making mechanism to handle uncertainty in negotiation. The experimental results show, both from the standard mean-score perspective and the perspective of empirical game theory, that the agent applying the proposed approach outperforms the state-of-the-art negotiation agents from the recent Automated Negotiating Agents Competition (ANAC) in a variety of negotiation domains.
A full realization of alternative energy such as biofuels depends on the existence of a viable supply chain (SC) network. An agent-based simulation approach is pursued to understand the dynamics of the biofuels SC net...
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A full realization of alternative energy such as biofuels depends on the existence of a viable supply chain (SC) network. An agent-based simulation approach is pursued to understand the dynamics of the biofuels SC network. The interests of three SC actors are represented: users, biorefineries, and farmers. Each actor type has a binary decision option: adoption or non-adoption of biofuels. This SC network model is characterized by distributed control, time asynchrony, and resource contention among actors, who make decisions based on incomplete knowledge and delayed information. The decision dynamics of these actors are modeled using a computational ecosystem construct. A preliminary set of coupled payoff function for each actor type and each decision is developed to represent interdependencies among SC actors. The simulation model was used to evaluate three archetypes of subsidy policy. The SC network behavior is observed in terms of fraction of actors adopting the biofuel option. The SC network shows behaviors ranging from fixed point equilibrium under no delay and perfect knowledge to periodic and chaotic oscillations. The network behavior is very sensitive to the time delay parameter that partly influences the quality of information on which actors' decisions are based. Several regions of SC behavior are identified. In particular, a chaotic behavior was observed. The work provides a methodological basis for further development, including identification of policies to control undesirable system behaviors.
This paper describes the performance measurement for multiagent systems (PeMMAS) tool, a system designed to study and measure the performance of any multiagent system (MAS) developed in JADE. The tool itself is anothe...
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This paper describes the performance measurement for multiagent systems (PeMMAS) tool, a system designed to study and measure the performance of any multiagent system (MAS) developed in JADE. The tool itself is another MAS which is deployed and coexists alongside the one being studied. This characteristic allows us to adapt PeMMAS to any scenario in which MAS deployment in JADE is used. PeMMAS extracts information from the target MAS regarding the use of system resources, the flight time for comprehensive messages according to agent type, as well as the processing time for actions. After processing this information, PeMMAS sends a report to the final user for subsequent analysis.
An adaptive frequency-domain equaliser for the single carrier frequency division multiple access (SC-FDMA) system using the particle swarm optimisation (PSO) technique is proposed. Unlike the stochastic gradient and r...
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An adaptive frequency-domain equaliser for the single carrier frequency division multiple access (SC-FDMA) system using the particle swarm optimisation (PSO) technique is proposed. Unlike the stochastic gradient and recursive least squares algorithms, the PSO is known to have fast convergence which does not depend on the underlying structure. The cost function used in a PSO is formulated based on the respective structure of the equaliser, whether it is a linear equaliser or a decision feedback equaliser. The robustness of the proposed PSO algorithm is demonstrated on a high Doppler scenario. Furthermore, it is shown that the performance improves more when using re-randomisation. Finally, it is shown that the PSO-based frequency-domain equaliser is more computationally efficient than its time-domain counterpart.
Steven Brams's [(1994). Theory of moves. Cambridge University Press] Theory of Moves (TOM) is an alternative to traditional game theoretic treatment of real-life interactions, in which players choose strategies ba...
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Steven Brams's [(1994). Theory of moves. Cambridge University Press] Theory of Moves (TOM) is an alternative to traditional game theoretic treatment of real-life interactions, in which players choose strategies based on analysis of future moves and counter-moves that arise if game play commences at a specified start state and either player can choose to move first. In repeated play, players using TOM rationality arrive at nonmyopic equilibria. One advantage of TOM is its ability to model scenarios in which power asymmetries exist between players. In particular, threat power, i.e. the ability of one player to threaten and sustain immediate, globally disadvantageous outcomes to force a desirable result long term, can be utilised to induce Pareto optimal states in games such as Prisoner's Dilemma which result in Pareto-dominated outcomes using traditional methods. Unfortunately, prior work on TOM is limited by an assumption of complete information. This paper presents a mechanism that can be used by a player to utilise threat power when playing a strict, ordinal 2x2 game under incomplete information. We also analyse the benefits of threat power and support in this analysis with empirical evidence.
Because of high speed, efficiency, robustness and flexibility of multi-agent systems, in recent years there has been an increasing interest in the art of these systems. artificial market mechanisms are one of the well...
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Because of high speed, efficiency, robustness and flexibility of multi-agent systems, in recent years there has been an increasing interest in the art of these systems. artificial market mechanisms are one of the well-known negotiation multi-agent protocols in multi-agent systems. In this paper artificial capital market as a new variant of market mechanism is introduced and employed in a multi-robot foraging problem. In this artificial capital market, the robots are going to benefit via investment on some assets, defined as doing foraging task. Each investment has a cost and an outcome. Limited initial capital of the investors constrains their investments. A negotiation protocol is proposed for decision making of the agents. Qualitative analysis reveals speed of convergence, near optimal solutions and robustness of the algorithm. Numerical analysis shows advantages of the proposed method over two previously developed heuristics in terms of four performance criteria.
Self-assembly enables nature to build complex forms, from multicellular organisms to complex animal structures such as flocks of birds, through the interaction of vast numbers of limited and unreliable individuals. Cr...
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Self-assembly enables nature to build complex forms, from multicellular organisms to complex animal structures such as flocks of birds, through the interaction of vast numbers of limited and unreliable individuals. Creating this ability in engineered systems poses challenges in the design of both algorithms and physical systems that can operate at such scales. We report a system that demonstrates programmable self-assembly of complex two-dimensional shapes with a thousand-robot swarm. This was enabled by creating autonomous robots designed to operate in large groups and to cooperate through local interactions and by developing a collective algorithm for shape formation that is highly robust to the variability and error characteristic of large-scale decentralized systems. This work advances the aim of creating artificial swarms with the capabilities of natural ones.
We propose protocol for automated negotiations between multiple agents over multiple and interdependent issues. We consider the situation in which the agents have to agree upon one option (contract) among many possibl...
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ISBN:
(纸本)9783319074559;9783319074542
We propose protocol for automated negotiations between multiple agents over multiple and interdependent issues. We consider the situation in which the agents have to agree upon one option (contract) among many possible ones (contract space). Interdependency between issues prevents us from applying negotiation protocols that have linear time complexity cost like Hill Climbing implementing mediated text negotiation protocol(HC). As a result most previous works propose methods in which the agents use non linear optimizers like simulated annealing to generate proposals. Then a central mediator can be used to match the proposals in order to find an intersection. But this matching process usually has exponential time cost complexity. We propose multi round HC(MR-HC) for negotiations with multiple and interdependent issues. In each round the normal HC is used to determine a negotiation deal region to be used by the next round. We propose that the agents should cluster their constraints by the cardinality of the constraints in order to get socially optimal contracts before applying MR-HC. To showcase that our proposed clustering technique is an essential one, we evaluate the optimality of our proposed protocol by running simulations at different cluster sizes.
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