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Creating Relationships with Computers and Robots 7an Air Force combat task. Focusing on traits ‘anxiety’, ‘aggressiveness’, and‘obsessiveness’, the prototype uses a knowledge-based approach to assess andadapt to the pilot’s anxiety level by means of different task-specific compensatorystrategies implemented in terms of specific GUI adaptations. One of thefocal goals of this research is to increase the realism of social intelligent agentsin situations where individual adaptation to the user is crucial, as in the criticalapplication reported here.Chapter 7, by Sebastiano Pizzutilo, Berardina De Carolis, and Fiorella DeRosis discusses how cooperative interface agents can be made more believablewhen endowed with a model that combines the communication traits describedin the Five Factor Model of personality (e.g., ‘extroverted’ versus ‘introverted’)with some cooperation attitudes. Cooperation attitudes refer in this case to thelevel of help that the agent provides to the user (e.g., an overhelper agent, aliteral helper agent), and the level of delegation that the user adopts towardsthe agent (e.g., a lazy user versus a ‘delegating-if-needed’ one). The agentimplements a knowledge-based approach to reason about and select the mostappropriate response in every context. The authors explain how cooperationand communication personality traits are combined in an embodied animatedcharacter (XDM-Agent) that helps users to handle electronic mail using Eudora.In chapter 8, Lola Cañamero reports the rationale underlying the constructionof Feelix, a very simple expressive robot built from commercial LEGOtechnology, and designed to investigate (facial) emotional expression for thesole purpose of social interaction. Departing from realism, Cañamero’s approachadvocates the use of a ‘minimal’ set of expressive features that allowhumans to recognize and analyze meaningful basic expressions. A clear causalpattern of emotion elicitation—in this case based on physical contact—is alsonecessary for humans to attribute intentionality to the robot and to make senseof its displays. Based on results of recognition tests and interaction scenarios,Cañamero then discusses different design choices and compares them withsome of the guidelines that inspired the design of other expressive robots, inparticular Kismet (cf. chapter 18). The chapter concludes by pointing out someof the ‘lessons learned’ about emotion from such a simple robot.Chapter 9, by Valery Petrushin, investigates how well people and computerscan recognize emotions in speech, and how to build an agent that recognizesemotions in speech signal to solve practical, real-world problems. Motivatedby the goal of improving performance at telephone call centers, this researchaddresses the problem of detecting emotional state in telephone calls with thepurpose of sorting voice mail messages or directing them to the appropriateperson in the call center. An initial research phase, reported here, investigatedwhich features of speech signal could be useful for emotion recognition, andexplored different machine learning algorithms to create reliable recognizers.