Elements of photogrammetry wolf pdf

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For permission to reuse our content please locate the material that you wish to use on link. Agent-based models are a kind of microscale model that simulate the simultaneous operations and interactions of multiple agents in an attempt to re-create and predict the appearance of complex phenomena. The idea of agent-based modeling was developed as a relatively simple concept in the late 1940s. Since it requires computation-intensive procedures, it did not become widespread until the 1990s. The history of the agent-based model can be traced back to the Von Neumann machine, a theoretical machine capable of reproduction. The device von Neumann proposed would follow precisely detailed instructions to fashion a copy of itself. One of the earliest agent-based models in concept was Thomas Schelling’s segregation model, which was discussed in his paper “Dynamic Models of Segregation” in 1971.

In the early 1980s, Robert Axelrod hosted a tournament of Prisoner’s Dilemma strategies and had them interact in an agent-based manner to determine a winner. The first use of the word “agent” and a definition as it is currently used today is hard to track down. One candidate appears to be John Holland and John H. Miller’s 1991 paper “Artificial Adaptive Agents in Economic Theory”, based on an earlier conference presentation of theirs. The 1990s were especially notable for the expansion of ABM within the social sciences, one notable effort was the large-scale ABM, Sugarscape, developed by Joshua M. Through the mid-1990s, the social sciences thread of ABM began to focus on such issues as designing effective teams, understanding the communication required for organizational effectiveness, and the behavior of social networks.

More recently, Ron Sun developed methods for basing agent-based simulation on models of human cognition, known as cognitive social simulation. Most computational modeling research describes systems in equilibrium or as moving between equilibria. Agent-based modeling, however, using simple rules, can result in different sorts of complex and interesting behavior. The three ideas central to agent-based models are agents as objects, emergence, and complexity. Agent-based models consist of dynamically interacting rule-based agents.

The systems within which they interact can create real-world-like complexity. Typically agents are situated in space and time and reside in networks or in lattice-like neighborhoods. The location of the agents and their responsive behavior are encoded in algorithmic form in computer programs. In some ways, agent-based models complement traditional analytic methods. Where analytic methods enable humans to characterize the equilibria of a system, agent-based models allow the possibility of generating those equilibria. This generative contribution may be the most mainstream of the potential benefits of agent-based modeling. Rather than focusing on stable states, many models consider a system’s robustness—the ways that complex systems adapt to internal and external pressures so as to maintain their functionalities.

The task of harnessing that complexity requires consideration of the agents themselves—their diversity, connectedness, and level of interactions. Recent work on the Modeling and simulation of Complex Adaptive Systems has demonstrated the need for combining agent-based and complex network based models. Complex Network Modeling Level for developing models using interaction data of various system components. Exploratory Agent-based Modeling Level for developing agent-based models for assessing the feasibility of further research. Building DREAM models allows model comparison across scientific disciplines.

The role of the environment where agents live, both macro and micro, is also becoming an important factor in agent-based modelling and simulation work. Simple environment affords simple agents, but complex environments generates diversity of behaviour. Agent-based models have been used since the mid-1990s to solve a variety of business and technology problems. In addition, ABMs have been used to simulate information delivery in ambient assisted environments. Agent based evolutionary search or algorithm is a new research topic for solving complex optimization problems. Graphic user interface for an agent-based modeling tool.