Rahmatollah Beheshti

Extracting Agent-Based Models of Human Transportation Patterns

Due to their cheap development costs and ease of deployment, surveys and questionnaires are useful tools for gathering information about the activity patterns of a large group and can serve as a valuable supplement to tracking studies done with mobile devices. However in raw form, general survey data is not necessarily useful for answering predictive questions about the behavior of a large social system. In this paper, we describe a method for generating agent activity profiles from survey data for an agent-based model (ABM) of transportation patterns of 47,000 students on a university campus. We compare the performance of our agent-based model against a Markov Chain Monte Carlo (MCMC) simulation based directly on the distributions fitted from the survey data. A comparison of our simulation results against an independently collected dataset reveals that our ABM can be used to accurately forecast parking behavior over the semester and is significantly more accurate than the MCMC estimator.

A hybrid modeling approach for parking and traffic prediction in urban simulations

Abstract Urban simulations are an important tool for analyzing many policy questions relating to the usage of public space, roads, and communal transportation; they can be used to predict the long-term impact of new construction projects, traffic restrictions, and zoning laws. However, it is unwise to rely upon predictions from a single model since each technique possesses different strengths and weaknesses and can be highly sensitive to the choice of parameters and initial conditions. In this article, we describe a hybrid approach for combining agent-based and stochastic simulations (Markov chain Monte Carlo, MCMC) to improve the accuracy and reduce the variance of long-term predictions. In our proposed approach, the agent-based model is used to bootstrap the proposal distribution for the MCMC estimator. To demonstrate the applicability of our modeling technique, this article presents a case study describing the usage of our hybrid simulation method for forecasting transportation patterns and parking lot utilization on a large university campus. A comparison of our simulation results against an independently collected dataset reveals that our hybrid approach accurately predicts parking lot usage and performs significantly better than other comparable modeling techniques. Developing novel architectures for combining the predictions of agent-based models can produce insights that are different than simply selecting the best model.

HOMAN, a learning based negotiation method for holonic multi-agent systems

Holonic multi-agent systems are a special category of multi-agent systems that best fit to environments with numerous agents and high complexity. Like in general multi-agent systems, the agents in the holonic system may negotiate with each other. These systems have their own characteristics and structure, for which a specific negotiation mechanism is required. This mechanism should be simple, fast and operable in real world applications. It would be better to equip negotiators with a learning method which can efficiently use the available information. The learning method should itself be fast, too. Additionally, this mechanism should match the special characteristics of the holonic multi-agent systems. In this paper, we introduce such a negotiation method. Experimental results demonstrate the efficiency of this new approach.

Improving markov chain monte carlo estimation with agent-based models

The Markov Chain Monte Carlo (MCMC) family of methods form a valuable part of the toolbox of social modeling and prediction techniques, enabling modelers to generate samples and summary statistics of a population of interest with minimal information. It has been used successfully to model changes over time in many types of social systems, including patterns of disease spread, adolescent smoking, and geopolitical conflicts. In MCMC an initial proposal distribution is iteratively refined until it approximates the posterior distribution. However, the selection of the proposal distribution can have a significant impact on model convergence. In this paper, we propose a new hybrid modeling technique in which an agent-based model is used to initialize the proposal distribution of the MCMC simulation. We demonstrate the use of our modeling technique in an urban transportation prediction scenario and show that the hybrid combined model produces more accurate predictions than either of the parent models.

Analyzing Agent-Based Models Using Category Theory

Agent-based models are a useful technique for rapidly prototyping complex social systems, they are widely used in a number of disciplines and can yield theoretical insights that are different from those produced by a variable based analysis. However, it remains difficult to compare the results of two models and to validate the performance of an agent-based simulation. In this paper, we present a case study on how to analyze the relationship between agent-based models using category theory. Category theory is a powerful mathematical methodology that was originally introduced to organize mathematical ideas according to their shared structure. It has been successfully employed in abstract mathematical domains, but has also enjoyed some success as a formalism for software engineering. Here we present a procedure for analyzing agent-based models using category theory and a case study in its usage at analyzing two different types of simulations.

Negotiations in Holonic Multi-agent Systems

Holonic multi-agent systems (HOMAS) have their own properties that make them distinct from general multi-agent systems (MAS). They are neither like competitive multi-agent systems nor cooperative, and they have features from both of these categories. There are many circumstances that holonic agents need to negotiate. Agents involved in negotiations try to maximize their utility as well as their holon’s utility. In addition, holon’s Head can overrule the negotiation whenever it wants. These differences make defining a specific negotiation mechanism for holonic multi-agent systems more significant. In this work, holonic systems are introduced at the beginning; and then different aspects of negotiation in these systems are studied. We especially try to introduce the idea of holonic negotiations. A specific negotiation mechanism for holonic multi-agent systems is proposed which is consistent with the challenges of HOMAS.

Modeling implicit collaboration with normative agent architectures

Summary form only given. Although there are many explicit collaboration mechanisms in which human teams commit to a joint project, a high level of implicit coordination can be achieved in human societies through the propagation of social norms. A norm can be defined as a “a behavioral rule that is considered valid by the majority of the population” [1]. Norms play a significant role in determining the behavior of people in human societies, and have been successfully used to achieve coordination within normative multi-agent systems.