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PART OF SPEECH TAGGING Natural Language Processing is an
PART OF SPEECH TAGGING Natural Language Processing is an

... ; [19] Where z(x) is a normalization factor over all ; CRF achieves accuracy of 98.05% in ...
Using Expectations to Drive Cognitive Behavior
Using Expectations to Drive Cognitive Behavior

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Decision support system - Austin Community College
Decision support system - Austin Community College

... AGENT-BASED MODELING • Biomimicry – learning from ecosystems and adapting their characteristics to human and organizational situations • Used to 1. Learn how people-based systems behave 2. Predict how they will behave under certain circumstances 3. Improve human systems to make them more efficient a ...
Neural characterization in partially observed populations of spiking
Neural characterization in partially observed populations of spiking

... Point process encoding models provide powerful statistical methods for understanding the responses of neurons to sensory stimuli. Although these models have been successfully applied to neurons in the early sensory pathway, they have fared less well capturing the response properties of neurons in de ...
A Belief-Desire-Intention Model for Narrative Generation
A Belief-Desire-Intention Model for Narrative Generation

... to where it is and pick it up; if it is a target character that wants to get some food, go to where the food is and wait for the target character to come by. Then chase them down. If an agent wants to chase some target character: if the target is safe, give up; if the target is right next to the age ...
Tim Menzies, Windy Gambetta Artificial Intelligence Laboratory
Tim Menzies, Windy Gambetta Artificial Intelligence Laboratory

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THE NATURE OF MODELING by Jeff Rothenberg Chapter for "AI
THE NATURE OF MODELING by Jeff Rothenberg Chapter for "AI

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記錄 編號 6668 狀態 NC094FJU00392004 助教 查核 索書 號 學校
記錄 編號 6668 狀態 NC094FJU00392004 助教 查核 索書 號 學校

... pp. 154-156. [14] J. Y. Kuo, “A document-driven agent-based approach for business”, processes management. Information and Software Technology, 2004, Vol. 46, pp. 373-382. [15] J. Y. Kuo, S.J. Lee and C.L. Wu, N.L. Hsueh, J. Lee. Evolutionary Agents for Intelligent Transport Systems, International Jo ...
記錄編號 6668 狀態 NC094FJU00392004 助教查核 索書號 學校名稱
記錄編號 6668 狀態 NC094FJU00392004 助教查核 索書號 學校名稱

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컴퓨터과학 입문 An Introduction to Computer Science.

... – understand the notion of an agent, how agents are distinct from other software paradigms (e.g., objects), and understand the characteristics of applications that lend themselves to an agentoriented solution; – understand the key issues associated with constructing agents capable of intelligent aut ...
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Agent-based model in biology

Agent-based models have many applications in biology, primarily due to the characteristics of the modeling method. Agent-based modeling is a rule-based, computational modeling methodology that focuses on rules and interactions among the individual components or the agents of the system. The goal of this modeling method is to generate populations of the system components of interest and simulate their interactions in a virtual world. Agent-based models start with rules for behavior and seek to reconstruct, through computational instantiation of those behavioral rules, the observed patterns of behavior. Several of the characteristics of agent-based models important to biological studies include: Modular structure: The behavior of an agent-based model is defined by the rules of its agents. Existing agent rules can be modified or new agents can be added without having to modify the entire model. Emergent properties: Through the use of the individual agents that interact locally with rules of behavior, agent-based models result in a synergy that leads to a higher level whole with much more intricate behavior than those of each individual agent. Abstraction: Either by excluding non-essential details or when details are not available, agent-based models can be constructed in the absence of complete knowledge of the system under study. This allows the model to be as simple and verifiable as possible. Stochasticity: Biological systems exhibit behavior that appears to be random. The probability of a particular behavior can be determined for a system as a whole and then be translated into rules for the individual agents.Before the agent-based model can be developed, one must choose the appropriate software or modeling toolkit to be used. Madey and Nikolai provide an extensive list of toolkits in their paper ""Tools of the Trade: A Survey of Various Agent Based Modeling Platforms"". The paper seeks to provide users with a method of choosing a suitable toolkit by examining five characteristics across the spectrum of toolkits: the programming language required to create the model, the required operating system, availability of user support, the software license type, and the intended toolkit domain. Some of the more commonly used toolkits include Swarm, NetLogo, RePast, and Mason. Listed below are summaries of several articles describing agent-based models that have been employed in biological studies. The summaries will provide a description of the problem space, an overview of the agent-based model and the agents involved, and a brief discussion of the model results.
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