The real-time simulation of human crowds has many applications. In a typical crowd simulation, each person ('agent') in the crowd moves towards a goal while adhering to local constraints. Many algorithms exist for specific local ‘steering’ tasks such as collision avoidance or group behavior. However, these do not easily extend to completely new types of behavior, such as circling around another agent or hiding behind an obstacle. They also tend to focus purely on an agent's velocity without explicitly controlling its orientation. This paper presents a novel sketch-based method for modelling and simulating many steering behaviors for agents in a crowd. Central to this is the concept of an interaction field (IF): a vector field that describes the velocities or orientations that agents should use around a given ‘source’ agent or obstacle. An IF can also change dynamically according to parameters, such as the walking speed of the source agent. IFs can be easily combined with other aspects of crowd simulation, such as collision avoidance. Using an implementation of IFs in a real-time crowd simulation framework, we demonstrate the capabilities of IFs in various scenarios. This includes game-like scenarios where the crowd responds to a user-controlled avatar. We also present an interactive tool that computes an IF based on input sketches. This IF editor lets users intuitively and quickly design new types of behavior, without the need for programming extra behavioral rules. We thoroughly evaluate the efficacy of the IF editor through a user study, which demonstrates that our method enables non-expert users to easily enrich any agent-based crowd simulation with new agent interactions.
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In social settings, people often need to reason about unobservablemental content of other people, such as their beliefs, goals, orintentions. This ability helps them to understand, to predict, and evento influence the behavior of others. People can take this ability furtherby applying it recursively. For example, they use second-order theory ofmind to reason about the way others use theory of mind, as in ‘Alicebelieves that Bob does not know about the surprise party’. However,empirical evidence so far suggests that people do not spontaneously usehigher-order theory of mind in strategic games. Previous agent-basedmodeling simulations also suggest that the ability to recursively applytheory of mind may be especially effective in competitive settings. Inthis paper, we use a combination of computational agents and Bayesianmodel selection to determine to what extent people make use of higherordertheory of mind reasoning in a particular competitive game, theMod game, which can be seen as a much larger variant of the well-knownrock-paper-scissors game.We let participants play the competitive Mod game against computationaltheory of mind agents. We find that people adapt their level oftheory of mind to that of their software opponent. Surprisingly, knowinglyplaying against second- and third-order theory of mind agents enticeshuman participants to apply up to fourth-order theory of mindthemselves, thereby improving their results in the Mod game. This phenomenoncontrasts with earlier experiments about other strategic oneshotand sequential games, in which human players only displayed lowerorders of theory of mind.
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Accurate modeling of end-users’ decision-making behavior is crucial for validating demand response (DR) policies. However, existing models usually represent the decision-making behavior as an optimization problem, neglecting the impact of human psychology on decisions. In this paper, we propose a Belief-Desire-Intention (BDI) agent model to model end-users’ decision-making under DR. This model has the ability to perceive environmental information, generate different power scheduling plans, and make decisions that align with its own interests. The key modeling capabilities of the proposed model have been validated in a household end-user with flexible loads
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