Integrating Models of Personality and Emotions into Lifelike Characters

André, Elisabeth; Klesen, Martin; Gebhard, Patrick; Allen, Steve; Rist, Thomas

doi:10.1007/10720296_11

Cited by 124 publications

(76 citation statements)

References 15 publications

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“…Although these features have been suggested by the psycholinguistic literature, reported correlations with personality ratings are generally weak: it was not obvious that they would improve accuracies of statistical models on unseen subjects. Computational work on modelling personality has primarily focused on methods for expressing personality in virtual agents and tutorial systems, and concepts related to personality such as politeness, emotion, or social intelligence (Walker, Cahn, & Whittaker, 1997;André, Klesen, Gebhard, Allen, & Rist, 1999;Lester, Towns, & FitzGerald, 1999;Wang, Johnson, Mayer, Rizzo, Shaw, & Collins, 2005) inter alia. Studies have shown that user evaluations of agent personality depend on the user's own personality (Reeves & Nass, 1996;Cassell & Bickmore, 2003), suggesting that an ability to model the user's personality is required.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text

Mairesse¹,

Walker²,

Mehl³

et al. 2007

jair

725

583

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It is well known that utterances convey a great deal of information about the speaker in addition to their semantic content. One such type of information consists of cues to the speaker's personality traits, the most fundamental dimension of variation between humans. Recent work explores the automatic detection of other types of pragmatic variation in text and conversation, such as emotion, deception, speaker charisma, dominance, point of view, subjectivity, opinion and sentiment. Personality affects these other aspects of linguistic production, and thus personality recognition may be useful for these tasks, in addition to many other potential applications. However, to date, there is little work on the automatic recognition of personality traits. This article reports experimental results for recognition of all Big Five personality traits, in both conversation and text, utilising both self and observer ratings of personality. While other work reports classification results, we experiment with classification, regression and ranking models. For each model, we analyse the effect of different feature sets on accuracy. Results show that for some traits, any type of statistical model performs significantly better than the baseline, but ranking models perform best overall. We also present an experiment suggesting that ranking models are more accurate than multi-class classifiers for modelling personality. In addition, recognition models trained on observed personality perform better than models trained using selfreports, and the optimal feature set depends on the personality trait. A qualitative analysis of the learned models confirms previous findings linking language and personality, while revealing many new linguistic markers.

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Section: Discussion and Future Workmentioning

confidence: 99%

Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text

Mairesse¹,

Walker²,

Mehl³

et al. 2007

jair

725

583

View full text Add to dashboard Cite

show abstract

“…This represents one aspect of a very significant effort to design emotion and personality models for agents (e.g. [2,17,10,38,32,1,15,31]). …”

Section: Discussionmentioning

confidence: 99%

Don’t Scratch! Self-adaptors Reflect Emotional Stability

Neff

Toothman

Bowmani

et al. 2011

Intelligent Virtual Agents

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Abstract. A key goal in agent research is to be able to generate multimodal characters that can reflect a particular personality. The Big Five model of personality provides a framework for codifying personality variation. This paper reviews findings in the psychology literature to understand how the Big Five trait of emotional stability correlates with changes in verbal and nonverbal behavior. Agent behavior was modified based on these findings and a perceptual study was completed to determine if these changes lead to the controllable perception of emotional stability in virtual agents. The results reveal how language variation and the use of self-adaptors can be used to increase or decrease the perceived emotional stability of an agent. Self-adaptors are movements that often involve self-touch, such as scratching or bending one's fingers backwards in an unnatural brace. These results provide guidance on how agent designers can create particular characters, including indicating that for particular personality types, it is important to also produce typically non-communicative gestural behavior, such as the self-adaptors studied.

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“…Our use of coping preferences to guide choice of coping strategies or behaviors is similar to [3,11,22]. However, this past work has relied on emotions only and not personality based on experience to constrain decision-making.…”

Section: Future Work Discussion and Conclusionmentioning

confidence: 99%

“…However this is a time consuming and expensive approach, and not even feasible when large numbers of different characters are needed. Most prior work that aims to create characters with emotions and personality has concentrated on one or two characters (such as [3]) or superficial crowd simulations (such as [2]). …”

Section: Introductionmentioning

confidence: 99%

Creating Adaptive and Individual Personalities in Many Characters Without Hand-Crafting Behaviors

Sandercock

Padgham

Zambetta

2006

Intelligent Virtual Agents

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Abstract. Believable characters significantly increase the immersion of users or players in interactive applications. A key component of believable characters is their personality, which has previously been implemented statically using the time consuming task of hand-crafting individuality for each character. Often personality has been modeled based on theories that assume behavior is the same regardless of situation and environment. This paper presents a simple affective and cognitive framework for interactive entertainment characters that allows adaptation of behavior based on the environment and emotions. Different personalities are reflected in behavior preferences which are generated based on individual experience. An initial version of the framework has been implemented in a simple scenario to explore which parameters have the greatest effect on agent diversity.

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Integrating Models of Personality and Emotions into Lifelike Characters

Cited by 124 publications

References 15 publications

Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text

Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text

Don’t Scratch! Self-adaptors Reflect Emotional Stability

Creating Adaptive and Individual Personalities in Many Characters Without Hand-Crafting Behaviors

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