Topic 5 Emotive States; Emotive Animat Emotions and Computers A Model of Emotions Emotional Expression for Communication Affective Model for Animats Sensations, Emotions, Moods and Feelings Portraying Emotions in Games An Extensible Affective System Processing Sensations & Emotions The Affective System Put Together Reading: Champandard Chapters 36, 37, 39 Links to Affective Computing, Kismet Robot on Website

ICT2192 Emotions and Computers The idea of computers with emotions may seem strange because i) our culture separates emotions and logic and ii) associates computers with logic Yet the truth is that models of emotions have been built from the earliest days of AI and “affective computing” is by now a well- established subfield There could be many advantages in making machines cable of having emotions, or at least recognising them in humans and reacting properly In nouvelle game AI, we are interested in making emotional animats for the same reason as story-tellers – because this will enhance the realism of non-player characters, make them more entertaining and help to more fully engage players with them A lot of interesting philosophical questions flow from the idea of emotional computers

ICT2193 A Model of Emotions Plutchick (1980) described eight primary emotions organised into pairs of states. In this model, it is impossible to experience the two states of a pair at once. The overall emotional status is registered by degrees of arousal of one of each pair Each state has a degree of intensity and a target object or state of affairs to which it refers Each state has behavioural implications which betray its “purpose” in helping the organism survive causes rejection of the target object or state of affairs/produces aversive signal for learning FEAR | ANGER ACCEPTANCE | DISGUST JOY | SORROW ANTICIPATION | SUPRISE recruits energy and directs destructive force toward an obstacle flags unexpected event/ directs attention to percepts/ raises heart rate releases hormones, tensions muscles and prepares for fight or flight recruits energy and produces reinforcing signal for learning withholds energy, forces withdrawal to safety and produces aversive signal for learning tolerates target object or state of affairs/produces reinforcing signal for learning allows preparation for expected event/ tests model of the world

ICT2194 A Model of Emotions The Plutchik model allows states to combine to produce compound “flavours” of emotion. Eg sorrow + suprise = disappointment; fear + acceptance = submission, etc. The model presupposes perceptual, learning and cognitive systems, but says little about them Since emotions are closely tied to sensors, effectors and bodily interaction in the world, it follows that embodied AI might be useful at providing these as a more complete system than traditional AI models, which had to work on disembodied computers Generally speaking, affective models of this kind try to mimic the different basic functions of emotions: regulation of behaviour, learning and communication Plutchik’s model is an example of how emotional states might signal survival-related classes of stimuli and how this could regulate action

ICT2195 Emotional Expression for Communication Emotion has an important role in dealing with others Being able to read the emotional state of another human would have powerful survival implications, and hence be selected for in human evolution Being able to influence the emotional state of another human would also be very useful Humans signal their emotional states by universal expressions, and we can read these in other humans It could be useful to have computers or robots do this: - vision systems could read human facial expressions and classify them as emotions - graphical or robot faces could have their features set into universal expressions to signal emotions

Emotional Expression for Communication Emotions can also be expressed in (and read from) gestures Over a longer time frame, emotions can be expressed in (and read from) behaviours Emotions may also be communicated via language: - choice of words may be influenced by emotional state - pitch, tone, speed and loudness of voice carries affective information This means that an emotional system needs to interact with other behavioural control mechanisms and influence them according to the current emotional state of the system So research on these areas often overlaps with the study of realistic body animation, including facial expression, mannerisms, natural language generation and speech synthesis

ICT2197 Affective Model for Animats We would like to try out our notions of affective AI in a FEAR animat In Chapter 37, Champandard advances a model that distinguishes a number of separate affective states in terms of their origin, longevity, and the ‘data’ they hold These are sensations, emotions, moods and feelings. Be careful – these are special technical definitions of these words in terms of C++ data structures and should not be confused with their usual meanings Once these are modeled, we can consider an interface to the FEAR game engine modules which communicate the effects from the AI model

ICT2198 Sensations Emotions in embodied creatures begin with sensations. In this model, a sensation will be defined as an immediate reaction to a creature’s current situation They might include surprise, anticipation, disgust, attraction, pain and pleasure Sensations are visceral “gut reactions” – they happen almost instantaneously, and do not last very long. They may arise from two different sources: Sensations from Perceptions – The body detects a certain class of stimuli from the environment, which causes an immediate short term reaction eg sensation of surprise if a player or NPC appears suddenly Sensations from Cognition – A mental state may also cause a sensation. For example, the thought of blood might cause a sensation of disgust

ICT2199 Emotions Higher-level and longer-lasting affective trends are called emotions in this model. An emotion is a lasting characteristic of a persons affective state Emotions might include fear, anger, joy and sorrow Emotions change slowly compared to sensations. Emotions could be triggered by a single sensation, or by multiple sensations Champardard calls basic dimensions (such as fear, anger etc) primary emotions. Each may have various degrees of intensity, which Champandard calls secondary emotions (?) Eg the primary emotion of fear might have a low intensity (apprehension) or a high intensity (terror) As with Plutchik’s model, some might be mutually antagonistic eg joy and sorrow. These are called complementary emotions.

ICT21910 Moods Now a creature’s mood will be defined as its complete set of emotions at a particular time This will be represented as a set of primary emotions, each with its own intensity A positive mood might include acceptance, satisfaction (moderate amount of joy) and awe (high level of surprise) Moods thus include complex emotions, which are subsets of primary emotions

ICT21911 Feelings Affective states can refer to an object or event (even one remembered in the past or anticipated in the future) In Champadard’s model, these are called feelings Feelings are persistent associations between an emotion and a (class of) object Examples are cruelty (to dogs), love (of a brother), hatred (of pirates) Feelings do not depend on the current state of the creature, but may refer to past or present (or even hypothetical) states of affairs

ICT21912 Portraying Emotions in Games How could an observer see the effects of emotional states in an animat which had them? Many games (certainly FEAR/Quake2) have little, if any provision for controlling facial expressions well enough to signal emotions that way (The action is usually too fast to see the faces close up anyway) However, recall that emotions can also be communicated by actions and language Existing actions can be triggered or changed by affective state. Eg, terror might cause the animat to wail, or depression might cause it walk more slowly than usual A lack of action might also signify something: eg failing to respond to attack might be a sign of submission. Higher level decisions, even at the strategic level, might also reveal emotions in some circumstances

ICT21913 Existing gestures such as Flipoff() may be used to signify (anger) Champandard describes a general-purpose interface to the enumerated action functions of the Quake 2 engine void Gesture ( const GestureType g ); Senses indirectly play a role in the portrayal of emotions, because they are closely linked to action – especially in reactive models A good example is anticipation, which has the effect in real creatures of heightening sensory awareness. The thresholds at which stimuli signifying possible danger are detected are lower than usual in this state Chat communications can also be used to make language signify emotions eg Say() or SayTeam() functions void Say ( const string& text, const GroupType g = everybody );

ICT21914 Portraying Emotions in Games Some affective behaviour scenarios: - Joy: dance or jump - Fear: faster but less accurate turns than usual; run faster - Extreme fear: stop thinking and freeze for a while - Disgust: turn away from reference stimulus; say “ugh” - Anticipation: swing gun wildly at any sound; slower walking than usual Extra touches for very emotive characters - Insult enemies during battle; perform taunting gestures - Run away screaming when the enemy is powerful - Keep a history of conflict, and target enemy based on grudges

ICT21915 An Extensible Affective System Champandard designs an affective system for FEAR animats that - is based on the model described so far (and the Plutchick psychoevolutionary model) - uses a FEAR finite state automata/machines module - works with other behavioural control architectures (since emotions are only part of a functioning cognitive system) and can be enhanced Simplified: only models four primary emotions: fear/anger, surprise/anticipation. These are complementary states, so one should observe no more than two at any time Simplified: each emotion is represented as a binary value: either fully activated or inactive (no secondary emotional levels here) Simplified: emotional states merely raise or lower the effectiveness of existing reactive behaviours

ICT21916 FEAR’s FSA/FSM Module The FEAR platform allows both Finite State Automata (recognisers, acceptors, parsers) and Finite State Machines (transducers) to be easily and efficiently implemented FSA – inputs are a sequence of symbols (eg from an vector of characters) which can trigger the state transitions. The output can be any kind of data, but usually sets a categorical variable (for classification) or a Boolean variable (for acceptance): bool Accept ( const vector & sequence ); Symbol Recognise( const vector & sequence ); FSM – one output for each input, so you need to either step a single symbol in and out, or else run an entire set of symbols and get a matching output set: Symbol Tick ( const Symbol in ); void Simulate(const vector & in vector & out );

ICT21917 XML for FSA/FSM Data Structures Specification for emotion FSM Specification for sensation FSA transition to target x (“anger/anticipation”) on input y (“aa”) state labeled a outputs symbol b (here, they are the same)

ICT21918 Processing Sensations Sensations will be simply perceived classes of objects or events in the environment, such as unexpected sounds, blood splats or explosions (cognitive sensations are not addressed in this prototype) A simple FSA, used as a recogniser, will accept perceptions or sequences of perceptions and move into a state representing the current sensation, at each Think cycle Anger/anticipation Anger/surprise Fear/surprise Fear/anticipation expected damage unexpected sound explosion item picked up fa fs as aa

ICT21919 Processing Emotions The mood of the system may be in any of four states, each consisting of a pair of (non-complementary) emotions. This enforces the constraint that complementary emotions may not be expressed at the same time The moods are fully interconnected, so sensations from the sensation FSA may move the system from any mood to any other fear/surprise fear/anticipation anger/surprise anger/anticipation fa aa fs fa fs as aa fa as aa fs Each state will output an associated change in the parameters used to control accuracy and power of actions; and the delay and precision of perceptual functions...

ICT21920 Emotional Output Accuracy is the randomness or error of an action: 1.0 means the commanded action is performed perfectly; 0.0 means the action is randomised Power is used to scale the magnitude of the action: 1.0 means the actions have their maximal value 0.0 means the action is frozen Delay alters perceptions. A delay of n means n seconds pass before a stimuli is recognised by perceptual functions Precision alters the location of objects in space by perceptual functions. A value of 1 means the position is exact; while 0 means that a large random number is added to the distance and angle Precision = 0.7 Power = 0.2 Delay = 0.0 Accuracy = 0.9 Precision = 0.9 Power = 0.4 Delay = 1.0 Accuracy = 0.2 Precision = 0.0 Power = 1.0 Delay = 0.3 Accuracy = 0.8 Precision = 0.1 Power = 0.9 Delay = 0.8 Accuracy = 0.3 surprise anticipation fear anger

ICT21921 Affective System Put Together affecting all actions and perceptions sensation pattern of parameters emotion FSM sensation FSA cycle of perceptions At each Think cycle a (possibly new) sensation may arise in the sensation FSA if it reaches an end state New sensations are passed asynchronously to the emotion FSM, which may change state to a new mood Each mood outputs its characteristic parameters which in turn affect both actions and perceptions

Evaluation of Affective System This design is (partly) implemented in the Moody animat The alterations to action and perceptual parameters do make a difference to the animat’s behaviour, but it is subtle and you need observe carefully to see the effects It would be possible to make the emotional system output more dramatic behaviour - easier to see, but possibly inappropriate at times Errors in perception and changes in speed of response are realistic emotional effects and do lead to game events which wouldn’t happen otherwise System is simple to adjust (though not as simple as Ch suggests) Transitions are all boolean – states do not rise and fall in degree, so no “secondary emotions” are possible. Also the emotions do not change Emotional states have no “hysteresis”, meaning they can change as fast as sensations – not true to the model

ICT21923 Summary The recognition and production of emotions in computers is possible, and there are many reasons to wish to do so In nature, emotions regular behaviour, assist learning and allow communication to other organisms Plutchick’s model of emotion specifies 8 primary states organised into mutually antagonistic pairs. Each state has behavioural, learning and communication implications Embodied animats in games are a good test bed for emotions, because they can play out these implications in the game world, and should be more entertaining as a result But the portrayal of emotions in games can be problematic, because the opportunities for facial expression, gestures etc. could be limited Champandard defines sensations, emotions, moods and feelings technically, as part of an application of Plutchick’s model to the design of a FEAR animat (Moody) This model uses a FSA to transform perceptions into sensations The sensations drive a FSM model of emotional states, each of which modifies the efficiency and speed of perceptions and actions in the Moody animat