Emotive and cognitive simulations by agents: Roles of three levels of information processing

Kazemifard, Mohammad; Ghasem-Aghaee, Nasser; Ören, Tuncer I.

doi:10.1016/j.cogsys.2010.10.002

Cited by 18 publications

(8 citation statements)

References 11 publications

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“…Emotive language means the speakers' or the writers' use of words and phrases that stir, evoke, and generate the ISSN 1110-2721 listeners' or readers' emotions and feelings (Wallace et al, 2012). Speakers tend to use emotive language when describing their feelings or reflecting a subjunctive attitude (Kazemifard et al, 2012). If the speakers use emotive language properly and effectively, it enables the audiences to react in a particular way.…”

Section: Emotive Languagementioning

confidence: 99%

Lexico-Semantic Manipulative Devices in the 2016 U.S. Presidential Debates

Khedr¹

2021

CDELT Occasional Papers in the Development of English Education

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This study examines some of the lexico-semantic manipulative devices in the 2016 U.S. presidential debates between Hillary Clinton and Donald Trump. The lexico-semantic manipulative devices included in this study are conceptual metaphors, equivocations, and emotive language. The study concluded that Donald Trump used the three lexico-semantic manipulative devices more than his opponent. It also indicated that while emotive language was the most dominant used device, equivocations are the least used. It also showed that of the twenty different categories of conceptual metaphors, object metaphors were the most used category. The study also concluded that the different categories of conceptual metaphors help in both making the abstract ideas clearer and creating the image of the two candidates' political persona. The study clarified the important role played by the emotive language in evoking the audiences' emotions and feelings. The study also concluded that manipulation can easily finds its way through the skillful use of the four categories of equivocations (sender, receiver, context, and content).

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Section: Emotive Languagementioning

confidence: 99%

Lexico-Semantic Manipulative Devices in the 2016 U.S. Presidential Debates

Khedr¹

2021

CDELT Occasional Papers in the Development of English Education

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“…Agent simulation is already used to implement understanding human emotions including emotional intelligence, namely understanding emotions of others as well as of self [16,17]. Some other examples are: simulation of an anger agent [6], a team's interaction [18,22], emotional-social negotiator agent [33], emotion modeling [4,18,19,20] and emotion processing [17,36].…”

Section: Machine Understanding In Agent Simulationmentioning

confidence: 99%

Machine Understanding in Agent-Directed Simulation: State-of-the-Art and Research Directions

Ören

Yılmaz

Ghasem-Aghaee

et al. 2016

Modeling and Simulation of Complexity in Intelligent, Adaptive and Autonomous Systems 2016 (MSCIAAS 2016) and Space Simulation

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To set the stage, the comprehensive big picture of simulation is evoked and references to get detailed information are given. As part of the powerful synergy of software agents and simulation, contribution of software agents to simulation is outlined and proper references are given. The contribution of agents to simulation leads to agent simulation, agent-supported simulation, and agentmonitored simulation. Machine understanding which is an important aspect of computational intelligence, is outlined. Scope of machine understanding is specified, a functional decomposition of machine understanding systems is explained and elements of machine understanding systems are identified. After briefly explaining four categories and two stages of machine understanding, appropriate references are given. After this background, state-of-the-art of contribution of several types of machine understanding to three aspects of agent-directed simulation are listed and references provided. This part consists of contribution of machine understanding to agent simulation, to agentsupported simulation, and to agent-monitored simulation. For each three categories, some desirable research directions are also outlined.

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“…There are several prior arts on modeling agent behavior in response to a stimuli [19]. Given our focus on integration with real-world models and implementation, we define an (extensible) list of principal sensitivity parameters that empirically play a role in decision-making and can be estimated from real-world data or be modeled using distributions.…”

Section: Modeling Dr Signal Response and Actionsmentioning

confidence: 99%

DRSim: A cyber physical simulator for Demand Response systems

Wijaya

Banerjee

Ganu

et al. 2013

2013 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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Abstract-Demand Response (DR) is a mechanism in which electricity consumers alter their demand in response to power grid's supply and economic conditions. DR programs have the potential to improve resource efficiency, sustainability, grid reliability and economic viability by providing tighter alignment between demand and supply. However, implementing DR program is a non-trivial task as it requires good knowledge of electricity consumption preferences, economic models and contextual factors. Developing such knowledge through real world studies can be expensive and be time consuming. As a result, utility companies have been finding it complicated to analyze potential viability and return on investments of DR programs for various 'what-if' scenarios.To address this problem, we present DRSim -a cyberphysical simulator that allows utility companies to study demand side participation aspects of communities with various practical scenarios. DRSim is based on the principles of agent-oriented modeling of users' behavior and context. It is able to model the emergent behavior of a community based on real data traces that contain partial information about the environment. DRSim is a highly extensible framework to accommodate new data sources, new analytical functionalities and evolve its modeling power. Feasibility experiments show the modeling and analysis potential of DRSim in practical settings. I. INTRODUCTIONTowards a more sustainable environment, several governments have defined specific goals for reducing total energy consumption (e.g., EU aims to reduce energy consumption by 20% by 2020). With the emergence of technology to support Demand Response (DR), DR programs are known to be effective for reducing energy consumption and peak demand [1], [2]. These DR systems send an "alter demand" signal to electricity customers to alter their consumption at critical times, e.g. during supply-demand imbalance or in response to grid market price fluctuations.While residential power consumption forms a substantial portion of total energy consumption [3], [4], little is known today on -how much energy is consumed by a particular activity, what human tasks lead to peak usage times, how consumption varies across different customer segments, and which DR programs (e.g. price based [5] or incentive based) are effective for the specific communities. Moreover, the residential DR programs have a major challenge of receiving a good DR participation from a large number of small electricity consumers, where there is much less control and more autonomy [6], [7].The success of a residential DR program largely depends on proper understanding regarding the driving factors of the energy demand for the community and deriving useful conclusions on the demand dynamics. There are various factors

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Emotive and cognitive simulations by agents: Roles of three levels of information processing

Cited by 18 publications

References 11 publications

Lexico-Semantic Manipulative Devices in the 2016 U.S. Presidential Debates

Lexico-Semantic Manipulative Devices in the 2016 U.S. Presidential Debates

Machine Understanding in Agent-Directed Simulation: State-of-the-Art and Research Directions

DRSim: A cyber physical simulator for Demand Response systems

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