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Modeling Social Emotions and Social Attributions using barcode generator for none control to generate, create none image in none applications.c# generate high quality barcodes 3.2.2 Appraise the Causal Int none none erpretation Appraisal is performed by elaboration rules that trigger automatically and in parallel based on changes to working memory.

For example, if an addstep operator adds a new operator to the plan, elaboration rules automatically re to assess the signi cance of this new action from the perspective of the agent s goals: Does the action have an effect that facilitates or inhibits certain desired states How does this action have an impact on the likelihood of goal achievement, etc These conclusions are represented by explicit appraisal frames stored in working memory. A separate frame exists for each state object represented in working memory and these are automatically created or modi ed as a side effect of operators manipulating the causal interpretation. 3.

2.3 Construct Emotion Instances Emotion instances are generated automatically and in parallel from appraisal rules operating on the appraisal variables listed in each appraisal frame. One or more objects representing an emotion type and intensity are associated with the appraisal frame that generates them.

The emotion type of the instance is determined by a xed mapping based on the con guration of appraisal variables. For example, a frame with low desirability and high likelihood would yield to intense fear. 3.

2.4 Determine Emotional State EMA uses an activation-based sub-symbolic process, modeled outside of the Soar architecture and loosely motivated by ACT-R, to identify a particular emotional instance to exhibit and cope with. This activation is based on two factors: (1) how recently cognitive structures associated by the instance were touched by a Soar operator, and (2) how congruent the instance is to the other emotion instances in memory (this latter factor is intended to account for mood-congruent effects of emotion).

For the activation factor, each time a Soar operator accesses an element of the causal interpretation that has an associated appraisal frame, this frame is assigned an activation level equal to its intensity (this currently decays to zero upon the next application of a Soar operator). For example, an add-step operator would tend to activate an instance of hope that the step will address the threat and fear that the goal is threatened. For the congruence factor, EMA communicates the type and intensity of all current instances to a module that decays their intensities according to a xed rate and sums the intensities of instances of a given type into an overall score that can be viewed as the agent s mood (e.

g., there is an overall fear score that consists of the sum of the intensities of each instance of fear). A small fraction of this mood vector is added to the activation-level of activated instances.

The instance with the most activation becomes the emotion to be displayed and coped with.. upc barcode Jonathan Gratch, Wenji Mao, and Stacy Marsella 3.2.5 Propose and Adopt a Cop none for none ing Strategy Soar elaboration rules propose individual coping strategies that could potentially address the emotion instance identi ed in the previous stage.

The strategy itself is implemented by a Soar operator and each of these operators is proposed in parallel but only one is ultimately selected by Soar to sequentially apply. 3.3 Limitations and Related Work EMA relates to a number of past appraisal models of emotion.

Although we are perhaps the rst to provide an integrated account of coping, computational accounts of appraisal have advanced considerably over the years. In terms of these models, EMA contributes primarily to the problem of developing general and domain-independent algorithms to support appraisal, and by extending the range of appraisal variables amenable to a computational treatment. Early appraisal models focused on the mapping between appraisal variables and behavior and largely ignored how these variables might be derived, instead requiring domain-speci c schemes to derive their value variables.

For example, Elliott s (1992) Affective Reasoner, based on the OCC model (1988), required a number of domain speci c rules to appraise events. A typical rule would be that a goal at a football match is desirable if the agent favors the team that scored. More recent approaches have moved toward more abstract reasoning frameworks, largely building on traditional arti cial intelligence techniques.

For example El Nasr and colleagues (2000) use markov decision processes (MDP) to provide a very general framework for characterizing the desirability of actions and events. An advantage of this method is that it can represent indirect consequences of actions by examining their impact on future reward (as encoded in the MDP), but it retains the key limitations of such models: they can only represent a relatively small number of state transitions and assume xed goals. The closest approach to what we propose here is WILL (Moffat & Frijda, 1995), which ties appraisal variables to an explicit model of plans (which capture the causal relationships between actions and effects), although they, also, did not address the issue of blame/credit attributions, or how coping might alter this interpretation.

We build on these prior models, extending them to provide better characterizations of causality and the subjective nature of appraisal that facilitates coping. There are several obvious limitations in the current model. The model could be viewed as overemphasizing the importance of task-oriented goals.

Many psychological theories refer to more abstract concepts such as egoinvolvement (Lazarus, 1991). Other theories, for example, the theory of Ortony, Clore, and Collins (1988), emphasize the importance of social norms or standards in addition to goal processing. For example, fornication may satisfy a personal goal but violate a social standard.

Our approach.
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