John S Gero Agents – Agent Simulations AGENT-BASED SIMULATIONS

John S Gero Agents – Agent Simulations Simulations in Design l Visual Simulations l e.g. renderings and models l Mathematical Simulations l i.e. systems of equations l Physical Simulations l e.g. wind tunnels l Computational Simulations l e.g. finite element analysis l Agent-based Simulations l e.g. crowd simulations

John S Gero Agents – Agent Simulations Simulating Crowds l Craig Reynolds’ Flocking Algorithm l A subclass of Reynolds’ Steering Behaviours l Extended flocking algorithms for games l Additional behaviours for goal-oriented path-following l The Social Force Model l Simulated crowd behaviour based on empirical results

John S Gero Agents – Agent Simulations Flocks, Herds and Schools 1. Separation. Steer to avoid flockmates. 2. Cohesion. Steer to move toward the average position. 3. Alignment. Steer toward the average heading. 4. Avoidance. Steer to avoid running into obstacles. (a) Separation (b) Cohesion(c) Alignment (d) Avoidance Steering behaviours used in Reynold’s model of flocking.

John S Gero Agents – Agent Simulations The Social Force Model 1. Pedestrians are motivated to move as efficiently as possible to a destination. 2. Pedestrians wish to maintain a comfortable distance from other pedestrians. 3. Pedestrians wish to maintain a comfortable distance from obstacles. 4. Pedestrians may be attracted to other pedestrians or objects (e.g. posters).

John S Gero Agents – Agent Simulations SITUATED ANALYSIS Pedestrians may be attracted to other pedestrians or objects. Pedestrians try to maintain a comfortable distance from obstacles like walls. Pedestrians try to maintain a comfortable distance from other pedestrians. Pedestrians try to move as efficiently as possible to a destination. Description of situated social force pedestrian obstacle destination attraction repulsion Designing doors

John S Gero Agents – Agent Simulations Narrow door

John S Gero Agents – Agent Simulations Wide door

John S Gero Agents – Agent Simulations Two doors

John S Gero Agents – Agent Simulations INTEREST IN EMERGENT BEHAVIOUR

John S Gero Agents – Agent Simulations Agent-Centric Design Evaluations l Efficiency l Inefficiency is measured with respect to the deviation of an agent’s actual walking speed from it’s desired walking speed. l Comfort l Discomfort is measured with respect to the number of changes in direction that have to be made by an agent to navigate a space. l Non-homogenous crowd simulations l e.g. simulating crowds of pedestrians with different desired walking speeds.

John S Gero Agents – Agent Simulations A Curious Agent The architecture of a curious agent.

John S Gero Agents – Agent Simulations Detecting Novelty 1. How often similar patterns have been experienced. 2. How similar these patterns have been. 3. How recently these patterns have been experienced.

John S Gero Agents – Agent Simulations Calculating Interestingness hedonic value = reward + punish

John S Gero Agents – Agent Simulations The Curious Social Force Model l Extends the Helbing and Molnar’s Social Force Model l Adds an additional rule to the Social Force Model: Pedestrians are motivated to move towards potentially interesting areas l Models curious exploratory behaviour as a social force l Uses the same simple model of locomotion as flocking and the social force model to move agents l Incorporates learning and curiosity into the agent model l Situates agents in past experience to detect novelty in new experiences

John S Gero Agents – Agent Simulations Situated Design Evaluations l Agent-centric evaluation of designs l Evaluations of interestingness depends upon the hedonic function which may vary from one agent to another. l Evaluations situated in experiences of agents l Interestingness depends upon novelty detection which in turn depends upon the long term memory of the agent. l Situatedness and changing evaluations l The experience of a design changes how an agent will evaluated it in the future.

MIT Class Spring 2002 An Example Design Problem: Curating a Gallery

John S Gero Agents – Agent Simulations Implementation l Sensing l Simple vision through raycasting l Perceiving l Perception of colours as hues l Learning & Novelty Detection l Self-organising maps l Planning & Moving l Generating and combining social forces

John S Gero Agents – Agent Simulations Sensing Simple vision implemented using raycasting.

John S Gero Agents – Agent Simulations Perceiving l Simple perception of hues l The artworks in the gallery are modelled as blocks of colour, the agents are only interested in the hues of these artworks allowing the sampled environment to be represented as a vector of single values (angles on the colour wheel). A colour wheel.

John S Gero Agents – Agent Simulations Learning & Novelty Detection l Learning l 1D Self-organising map l Novelty detection l Approximates detection of novelty based on similarity of previous experiences, the past frequency of similar experience and time since the last similar experience.

John S Gero Agents – Agent Simulations Planning & Moving l The Curious Social Force Model l Motivational forces are generated for all perceived objects in the direction of the object with a magnitude proportional to the object’s interestingness, the forces are then combined into a single curious social force by averaging their magnitudes and directions.

John S Gero Agents – Agent Simulations Emergent Design Problems

John S Gero Agents – Agent Simulations Emergent Design Problems l Overcrowding to avoid uninteresting (radical) artworks l The agents in the first room become overcrowded because the artworks that are visible in the second room are too different from those in the first and generate a curious social force blocking entry to the second room. l Neglect of artworks because of improper sequencing l The agents pass quickly through the last room because the artworks in this room are too different from those in the previous room, encouraging a rapid exit.

John S Gero Agents – Agent Simulations One Possible Solution to Emergent Design Problems

John S Gero Agents – Agent Simulations One Possible Solution to Emergent Design Problems l Improving the progression of artworks l Artworks in second and third room are swapped so that the difference between artworks in successive rooms is minimised. l Improved flow of agents between rooms l Agents are drawn into each new room as a result of the greater interest the agents have in experiencing similar-yet- different artworks to those that they have already seen. l Increased interest, efficiency and comfort l Better design improves situated and agent-centric evaluations.