Eric Aaron

Scalable nonlinear dynamical systems for agent steering and crowd simulation

Abstract We present a new methodology for agent modeling that is scalable and efficient. It is based on the integration of nonlinear dynamical systems and kinetic data structures. The method consists of three layers, which together model 3D agent steering, crowds and flocks among moving and static obstacles. The first layer, the local layer employs nonlinear dynamical systems theory to models low-level behaviors. It is fast and efficient, and it does not depend on the total number of agents in the environment. This dynamical systems-based approach also allows us to establish continuous numerical parameters for modifying each agents behavior. The second layer, a global environment layer consists of a specifically designed kinetic data structure to track efficiently the immediate environment of each agent and know which obstacles/agents are near or visible to the given agent. This layer reduces the complexity in the local layer. In the third layer, a global planning laye r, the problem of target tracking is generalized in a way that allows navigation in maze-like terrains, avoidance of local minima and cooperation between agents. We implement this layer based on two approaches that are suitable for different applications: One approach is to track the closest single moving or static target; the second is to use a pre-specified vector field, which may be generated automatically (with harmonic functions, for example) or based on user input to achieve the desired output. We also discuss how hybrid systems concepts for global planning can capitalize on both our layered approach and the continuous, reactive nature of our agent steering. We demonstrate the power of the approach through a series of experiments simulating single/multiple agents and crowds moving towards moving/static targets in complex environments.

Herbivore diet breadth mediates the cascading effects of carnivores in food webs

Significance This study shows the far-reaching effects of herbivore dietary specialization on the ecological and evolutionary dynamics of carnivore–herbivore–plant interactions. First, we test the long-standing hypothesis that dietary specialization of insect herbivores mediates the strength of bird predation on herbivores. Accounting for phylogenetic nonindependence of herbivores and plants, we show for the first time (to our knowledge) that dietary specialization of herbivore species is associated with reduced bird predation across an herbivore phylogeny, and that dietary specialization of herbivores increases the antipredator effects of camouflage and aposematism. Second, this study develops and finds support for the novel hypothesis that the proportion of dietary specialist species in a plant’s herbivore community predicts the degree of antiherbivore protection birds provide to plants. Predicting the impact of carnivores on plants has challenged community and food web ecologists for decades. At the same time, the role of predators in the evolution of herbivore dietary specialization has been an unresolved issue in evolutionary ecology. Here, we integrate these perspectives by testing the role of herbivore diet breadth as a predictor of top-down effects of avian predators on herbivores and plants in a forest food web. Using experimental bird exclosures to study a complex community of trees, caterpillars, and birds, we found a robust positive association between caterpillar diet breadth (phylodiversity of host plants used) and the strength of bird predation across 41 caterpillar and eight tree species. Dietary specialization was associated with increased enemy-free space for both camouflaged (n = 33) and warningly signaled (n = 8) caterpillar species. Furthermore, dietary specialization was associated with increased crypsis (camouflaged species only) and more stereotyped resting poses (camouflaged and warningly signaled species), but was unrelated to caterpillar body size. These dynamics in turn cascaded down to plants: a metaanalysis (n = 15 tree species) showed the beneficial effect of birds on trees (i.e., reduced leaf damage) decreased with the proportion of dietary specialist taxa composing a tree species’ herbivore fauna. We conclude that herbivore diet breadth is a key functional trait underlying the trophic effects of carnivores on both herbivores and plants.

A hybrid dynamical systems approach to intelligent low-level navigation

Animated characters may exhibit several kinds of dynamic intelligence when performing low-level navigation (i.e., navigation on a local perceptual scale): they decide among different modes of behavior selectively discriminate entities in the world around them, perform obstacle avoidance, etc. In this paper we present a hybrid dynamical system model of low-level navigation that accounts for the above-mentioned kinds of intelligence. In so doing, the model illustrates general ideas about how a hybrid systems perspective can influence and simplify such reactive/behavioral modeling for multi-agent systems. In addition, we directly employed our formal hybrid system model to generate animations that illustrate our navigation strategies. Overall, our results suggest that hierarchical hybrid systems may provide a natural framework for modeling elements of intelligent animated actors.

Hybrid System Models of Navigation Strategies for Games and Animations

The virtual worlds of computer games and similar animated simulations may be populated by autonomous characters that intelligently navigate in virtual cities. We concretely apply hybrid system theory and tools to model navigation strategies for virtual characters. In particular, we present hybrid systems for both low-level (local) and high-level (global) navigation strategies, and we describe how we modeled these systems using the hybrid system specification tool Charon. Further, we directly employed our hybrid system models to generate animations that demonstrate these navigation strategies. Overall, our results suggest that hybrid systems may be a natural framework for modeling aspects of intelligent virtual actors. We also present a small verification example for a simple navigation strategy, and we briefly discuss obstacles to widespread practical applicability of verification in this problem domain.

A Multiple Time-scale Computational Model of a Tumor and Its Micro Environment

Experimental evidence suggests that a tumors environment may be critical to designing successful therapeutic protocols: Modeling interactions between a tumor and its environment could improve our understanding of tumor growth and inform approaches to treatment. This paper describes an efficient, flexible, hybrid cellular automaton-based implementation of numerical solutions to multiple time-scale reaction-diffusion equations, applied to a model of tumor proliferation. The growth and maintenance of cells in our simulation depend on the rate of cellular energy (ATP) metabolized from nearby nutrients such as glucose and oxygen. Nutrient consumption rates are functions of local pH as well as local concentrations of oxygen and other fuels. The diffusion of these nutrients is modeled using a novel variation of random-walk techniques. Furthermore, we detail the effects of three boundary update rules on simulations, describing their effects on computational efficiency and biological realism. Qualitative and quantitative results from simulations provide insight on how tumor growth is affected by various environmental changes such as micro-vessel density or lower pH, both of high interest in current cancer research.

A Framework for Reasoning about Animation Systems

In this paper, we consider the potential for reasoning about animations in the language of hybrid dynamical systems (i.e., systems with both continuous and discrete dynamics). We begin by directly applying hybrid systems theory to animation, using a general-purpose hybrid system specification tool to generate multi-agent animations; this application also illustrates that hybrid system models can provide systematic, modular ways to incorporate low-level behavior into a design for higher-level behavioral modeling. We then apply the logical framework of hybrid systems to animation: We formally state properties of animation systems that may not be readily expressed in other frameworks; and we mechanically check a collision-avoidance property for a simple racelike game. This hybrid systems-oriented approach could improve our ability to reason about virtual worlds, thus improving our ability to create intelligent virtual agents.

Action selection and task sequence learning for hybrid dynamical cognitive agents

As a foundation for action selection and task-sequencing intelligence, the reactive and deliberative subsystems of a hybrid agent can be unified by a single, shared representation of intention. In this paper, we summarize a framework for hybrid dynamical cognitive agents (HDCAs) that incorporates a representation of dynamical intention into both reactive and deliberative structures of a hybrid dynamical system model, and we present methods for learning in these intention-guided agents. The HDCA framework is based on ideas from spreading activation models and belief-desire-intention (BDI) models. Intentions and other cognitive elements are represented as interconnected, continuously varying quantities, employed by both reactive and deliberative processes. HDCA learning methods-such as Hebbian strengthening of links between co-active elements, and belief-intention learning of task-specific relationships-modify interconnections among cognitive elements, extending the benefits of reactive intelligence by enhancing high-level task sequencing without additional reliance on or modification of deliberation. We also present demonstrations of simulated robots that learned geographic and domain-specific task relationships in an office environment.

Dynamic obstacle representations for robot and virtual agent navigation

This paper describes a reactive navigation method for autonomous agents such as robots or actors in virtual worlds, based on novel dynamic tangent obstacle representations, resulting in exceptionally successful, geometrically sensitive navigation. The method employs three levels of abstraction, treating each obstacle entity as an obstacle-valued function; this treatment enables extraordinary flexibility without pre-computation or deliberation, applying to all obstacles regardless of shape, including non-convex, polygonal, or arc-shaped obstacles in dynamic environments. The unconventional levels of abstraction and the geometric details of dynamic tangent representations are the primary contributions of this work, supporting smooth navigation even in scenarios with curved shapes, such as circular and figure-eight shaped tracks, or in environments requiring complex, winding paths.

A Framework for Dynamical Intention in Hybrid Navigating Agents

As a foundation for goal-directed behavior, the reactive and deliberative systems of a hybrid agent can share a single, unifying representation of intention. In this paper, we present a framework for incorporating dynamical intention into hybrid agents, based on ideas from spreading activation models and belief-desire-intention (BDI ) models. In this framework, intentions and other cognitive elements are represented as continuously varying quantities, employed by both sub-deliberative and deliberative processes: On the reactive level, representations support some real-time responsive task re-sequencing; on the deliberative level, representations support common logical reasoning. Because cognitive representations are shared across both levels, inter-level integration is straightforward. Furthermore, dynamical intention is demonstrably consistent with philosophical observations that inform conventional BDI models, so dynamical intentions function as conventional intentions. After describing our framework, we briefly summarize simple demonstrations of our approach, suggesting that dynamical intention-guided intelligence can potentially extend benefits of reactivity without compromising advantages of deliberation in a hybrid agent.

On the Complexity of the Multi-Robot, Multi-Depot Map Visitation Problem

This paper discusses the multi-robot, multidepot Map Visitation Problem, a multi-robot inspection problem in which a team of robots originating from multiple home base depots must visit a collection of previously identified critical locations in a two-dimensional navigation environment. In its precise focus on location inspection, it is related yet complementary to other inspection or surveillance problems such as boundary coverage or patrol. In the paper, we analyze graph representations and an agent model appropriate for the Map Visitation Problem, and we present complexity results for a variety of categories of map structures, including lines, rings, trees, and general graphs. In addition to complexity results, we present an algorithm for the Map Visitation Problem on trees that is optimal for single-robot problems and a second algorithm that is provably within a factor of two of optimal for two robots inspecting arbitrary graphs.