Spatial Modeling using Agents

Simulation is a decision support technique that is often used to represent systems of interest and to experiment with them. Multi-Agent Simulation (MAS) is increasingly being used for modeling systems that comprise of autonomous and interacting system components. In such systems, the interactions among the underlying system components may be dependent on their spatial characteristics (e.g., dimension and location in three-dimensional space). The work presented in this paper describes an agent-based approach to spatial modeling through the use of a case study in container loading. The contribution of this paper is the demonstration of the feasibility of using MAS for spatial, proximity-based modeling, wherein not only agent behavior but also their physical dimension and their location in the three dimensional space are key considerations

Spatial Behavior in Groups: an Agent-Based Approach

We present an agent-based model with the aim of studying how macro-level dynamics of spatial distances among interacting individuals in a closed space emerge from micro-level dyadic and local interactions. Our agents moved on a lattice (referred to as a room) using a model implemented in a computer program called P-Space in order to minimize their dissatisfaction, defined as a function of the discrepancy between the real distance and the ideal, or desired, distance between agents. Ideal distances evolved in accordance with the agent\'s personal and social space, which changed throughout the dynamics of the interactions among the agents. In the first set of simulations we studied the effects of the parameters of the function that generated ideal distances, and in a second set we explored how group macro-level behavior depended on model parameters and other variables. We learned that certain parameter values yielded consistent patterns in the agents\' personal and social spaces, which in turn led to avoidance and approaching behaviors in the agents. We also found that the spatial behavior of the group of agents as a whole was influenced by the values of the model parameters, as well as by other variables such as the number of agents. Our work demonstrates that the bottom-up approach is a useful way of explaining macro-level spatial behavior. The proposed model is also shown to be a powerful tool for simulating the spatial behavior of groups of interacting individuals.Spatial Behavior, Proxemics, Agent-Based Modeling, Minimum Dissatisfaction Model, Small Groups, Social Interaction

The discrete dynamics of small-scale spatial events: agent-based models of mobility in carnivals and street parades

Small-scale spatial events are situations in which elements or objects vary in such away that temporal dynamics is intrinsic to their representation and explanation. Someof the clearest examples involve local movement from conventional traffic modelingto disaster evacuation where congestion, crowding, panic, and related safety issue arekey features of such events. We propose that such events can be simulated using newvariants of pedestrian model, which embody ideas about how behavior emerges fromthe accumulated interactions between small-scale objects. We present a model inwhich the event space is first explored by agents using ?swarm intelligence?. Armedwith information about the space, agents then move in an unobstructed fashion to theevent. Congestion and problems over safety are then resolved through introducingcontrols in an iterative fashion and rerunning the model until a ?safe solution? isreached. The model has been developed to simulate the effect of changing the route ofthe Notting Hill Carnival, an annual event held in west central London over 2 days inAugust each year. One of the key issues in using such simulation is how the processof modeling interacts with those who manage and control the event. As such, thischanges the nature of the modeling problem from one where control and optimizationis external to the model to one where this is intrinsic to the simulation

Modeling Location Choice of Secondary Activities with a Social Network of Cooperative Agents

Activity-based models in transportation science focus on the description of human trips and activities. Modeling the spatial decision for so-called secondary activities is addressed in this paper. Given both home and work locations, where do individuals perform activities such as shopping and leisure? Simulation of these decisions using random utility models requires a full enumeration of possible outcomes. For large data sets, it becomes computationally unfeasible because of the combinatorial complexity. To overcome that limitation, a model is proposed in which agents have limited, accurate information about a small subset of the overall spatial environment. Agents are interconnected by a social network through which they can exchange information. This approach has several advantages compared with the explicit simulation of a standard random utility model: (a) it computes plausible choice sets in reasonable computing times, (b) it can be extended easily to integrate further empirical evidence about travel behavior, and (c) it provides a useful framework to study the propagation of any newly available information. This paper emphasizes the computational efficiency of the approach for real-world examples

Demography-based adaptive network model reproduces the spatial organization of human linguistic groups

The distribution of human linguistic groups presents a number of interesting and non-trivial patterns. The distributions of the number of speakers per language and the area each group covers follow log-normal distributions, while population and area fulfill an allometric relationship. The topology of networks of spatial contacts between different linguistic groups has been recently characterized, showing atypical properties of the degree distribution and clustering, among others. Human demography, spatial conflicts, and the construction of networks of contacts between linguistic groups are mutually dependent processes. Here we introduce an adaptive network model that takes all of them into account and successfully reproduces, using only four model parameters, not only those features of linguistic groups already described in the literature, but also correlations between demographic and topological properties uncovered in this work. Besides their relevance when modeling and understanding processes related to human biogeography, our adaptive network model admits a number of generalizations that broaden its scope and make it suitable to represent interactions between agents based on population dynamics and competition for space

Predictive World Models from Real-World Partial Observations

Cognitive scientists believe adaptable intelligent agents like humans perform reasoning through learned causal mental simulations of agents and environments. The problem of learning such simulations is called predictive world modeling. Recently, reinforcement learning (RL) agents leveraging world models have achieved SOTA performance in game environments. However, understanding how to apply the world modeling approach in complex real-world environments relevant to mobile robots remains an open question. In this paper, we present a framework for learning a probabilistic predictive world model for real-world road environments. We implement the model using a hierarchical VAE (HVAE) capable of predicting a diverse set of fully observed plausible worlds from accumulated sensor observations. While prior HVAE methods require complete states as ground truth for learning, we present a novel sequential training method to allow HVAEs to learn to predict complete states from partially observed states only. We experimentally demonstrate accurate spatial structure prediction of deterministic regions achieving 96.21 IoU, and close the gap to perfect prediction by 62% for stochastic regions using the best prediction. By extending HVAEs to cases where complete ground truth states do not exist, we facilitate continual learning of spatial prediction as a step towards realizing explainable and comprehensive predictive world models for real-world mobile robotics applications. Code is available at https://github.com/robin-karlsson0/predictive-world-models.Comment: Accepted for IEEE MOST 202

Spatial interactions in agent-based modeling

Agent Based Modeling (ABM) has become a widespread approach to model complex interactions. In this chapter after briefly summarizing some features of ABM the different approaches in modeling spatial interactions are discussed. It is stressed that agents can interact either indirectly through a shared environment and/or directly with each other. In such an approach, higher-order variables such as commodity prices, population dynamics or even institutions, are not exogenously specified but instead are seen as the results of interactions. It is highlighted in the chapter that the understanding of patterns emerging from such spatial interaction between agents is a key problem as much as their description through analytical or simulation means. The chapter reviews different approaches for modeling agents' behavior, taking into account either explicit spatial (lattice based) structures or networks. Some emphasis is placed on recent ABM as applied to the description of the dynamics of the geographical distribution of economic activities, - out of equilibrium. The Eurace@Unibi Model, an agent-based macroeconomic model with spatial structure, is used to illustrate the potential of such an approach for spatial policy analysis.Comment: 26 pages, 5 figures, 105 references; a chapter prepared for the book "Complexity and Geographical Economics - Topics and Tools", P. Commendatore, S.S. Kayam and I. Kubin, Eds. (Springer, in press, 2014

Agent-Based Urban Land Markets: Agent's Pricing Behavior, Land Prices and Urban Land Use Change

We present a new bilateral agent-based land market model, which moves beyond previous work by explicitly modeling behavioral drivers of land-market transactions on both the buyer and seller sides; formation of bid prices (of buyers) and ask prices (of sellers); and the relative division of the gains from trade from the market transactions. We analyze model output using a series of macro-scale economic and landscape pattern measures, including land rent gradients estimated using simple regression models. We first demonstrate that our model replicates relevant theoretical results of the traditional Alonso/Von Thünen model (structural validation). We then explore how urban morphology and land rents change as the relative market power of buyers and sellers changes (i.e., we move from a 'sellers' market' to a 'buyers' market'). We demonstrate that these strategic price dynamics have differential effects on land rents, but both lead to increased urban expansion