A Case Study: Improve Classification of Rare Events
... challenges many predictive modelers face today. In this paper, we use SAS® Enterprise Miner™ on a marketing data set to demonstrate and compare several approaches commonly used to handle imbalanced data problems in classification models. The approaches are based on cost-sensitive measures and sampli ...
... challenges many predictive modelers face today. In this paper, we use SAS® Enterprise Miner™ on a marketing data set to demonstrate and compare several approaches commonly used to handle imbalanced data problems in classification models. The approaches are based on cost-sensitive measures and sampli ...
Economic implications of software minds
... minds are software products, they can be copied, accelerated, and improved by economic activity. Our goal in the present manuscript is to explore the implications of a mathematical model of an economy heading toward a technological singularity due to such feedback effects. Specifically, we start fro ...
... minds are software products, they can be copied, accelerated, and improved by economic activity. Our goal in the present manuscript is to explore the implications of a mathematical model of an economy heading toward a technological singularity due to such feedback effects. Specifically, we start fro ...
The Flavian Amphitheater (Colosseum) in Rome: An Excellent
... simulation, whereas the AI engine extracts information from the environment and feeds it to the agent (such as there is an obstacle ahead). For the agents to achieve their goals, three aspects must be considered: The sensor system: only the sight has been included in this version, modelled as an ang ...
... simulation, whereas the AI engine extracts information from the environment and feeds it to the agent (such as there is an obstacle ahead). For the agents to achieve their goals, three aspects must be considered: The sensor system: only the sight has been included in this version, modelled as an ang ...
The “Structured Matcher” Paper
... Artificial intelligence is the computational study of how a system can perceive, reason, and act in complex environments. • agents that solve problems • agents that reason logically • agents that plan their actions • agents that can handle uncertainty • agents that use knowledge of world and self • ...
... Artificial intelligence is the computational study of how a system can perceive, reason, and act in complex environments. • agents that solve problems • agents that reason logically • agents that plan their actions • agents that can handle uncertainty • agents that use knowledge of world and self • ...
cody8
... 16.Summarise and publish any methodological conclusions gained from the experience of relating different models. An important aspect of this project is the parallel trials of the models in both computer simulations and with socially situated robots. It has often been found that in the transference f ...
... 16.Summarise and publish any methodological conclusions gained from the experience of relating different models. An important aspect of this project is the parallel trials of the models in both computer simulations and with socially situated robots. It has often been found that in the transference f ...
Introduction to Artificial Intelligence 236501
... problems. • Successes in fields that lend themselves to formal description, such as mathematics. • Obstacles to this approach: – Real-world problems are difficult to formalize – Computation time proves a barrier for realistic problems in practice. ...
... problems. • Successes in fields that lend themselves to formal description, such as mathematics. • Obstacles to this approach: – Real-world problems are difficult to formalize – Computation time proves a barrier for realistic problems in practice. ...
Population and Agent Based Models for Language Convergence
... perspective; only the dynamics of groups are taken into account. This abstraction makes the problem easier to study, but ignores several important factors. In particular, work based on the LDE typically assumes agents can interact with each other uniformly. This may be unrealistic, as many social ne ...
... perspective; only the dynamics of groups are taken into account. This abstraction makes the problem easier to study, but ignores several important factors. In particular, work based on the LDE typically assumes agents can interact with each other uniformly. This may be unrealistic, as many social ne ...
Model Construction in General Intelligence
... scopes. More comprehensive structures employ temporarily subordinated detail structures and thereby not only bring these into relation, but guide their instantiation. As a result, the the model as a whole determines its further application and development. But while structures that one puts in focus ...
... scopes. More comprehensive structures employ temporarily subordinated detail structures and thereby not only bring these into relation, but guide their instantiation. As a result, the the model as a whole determines its further application and development. But while structures that one puts in focus ...
full document - Intelligent Systems Laboratory
... entities in a general way. Prediction also carries a much heavier burden when it is regularly and continuously compared to actual behavior, as is the case in the application of the resulting model. However, it provides the opportunity to implement validation relatively easily. A predictive model is ...
... entities in a general way. Prediction also carries a much heavier burden when it is regularly and continuously compared to actual behavior, as is the case in the application of the resulting model. However, it provides the opportunity to implement validation relatively easily. A predictive model is ...
PowerPoint - University of Virginia, Department of Computer Science
... • To update the agent function, ,in light of observed performance of percept-sequence to action pairs – Does the agent control observations? What parts of state space to explore? Learn from trial and error – How do observations affect agent function? Change internal variables that influence ac ...
... • To update the agent function, ,in light of observed performance of percept-sequence to action pairs – Does the agent control observations? What parts of state space to explore? Learn from trial and error – How do observations affect agent function? Change internal variables that influence ac ...
pdf
... 4 Conclusion This article presents the language and software environment LEADSTO that has been developed especially to model and simulate dynamic processes in terms of both qualitative and quantitative concepts. It is, for example, possible to model differential and difference equations, and to comb ...
... 4 Conclusion This article presents the language and software environment LEADSTO that has been developed especially to model and simulate dynamic processes in terms of both qualitative and quantitative concepts. It is, for example, possible to model differential and difference equations, and to comb ...
The Site-Model Construction Component of the RADIUS Testbed
... • Buildings and other structures such as petroleum and water storage tanks. • Lines of Communication such as roads, railroad tracks, and other linear features such as rivers and streams. • Functional Areas such as parking lots, site perimeters, rail transfer points and other area features such as fo ...
... • Buildings and other structures such as petroleum and water storage tanks. • Lines of Communication such as roads, railroad tracks, and other linear features such as rivers and streams. • Functional Areas such as parking lots, site perimeters, rail transfer points and other area features such as fo ...
PowerPoint - University of Virginia, Department of Computer Science
... • To update the agent function in light of observed performance of percept-sequence to action pairs – Explore new parts of state space Learn from trial and error – Change internal variables that influence action selection ...
... • To update the agent function in light of observed performance of percept-sequence to action pairs – Explore new parts of state space Learn from trial and error – Change internal variables that influence action selection ...
Bringing the User Back into Scheduling: Two Case Studies of
... preference model for the user This dynamic model is used to derive the objective function by which scheduling options are evaluated, presented to the user, and scheduled ...
... preference model for the user This dynamic model is used to derive the objective function by which scheduling options are evaluated, presented to the user, and scheduled ...
A Development Environment for Engineering Intelligent
... Modeling multiagent systems (MAS) is a complex endeavour. An ideal domain specific agent modeling language would be tailored to a certain application domain (e.g. virtual worlds) as well as to the target execution environment (e.g. a legacy virtual reality platform). At the same time it is desirable ...
... Modeling multiagent systems (MAS) is a complex endeavour. An ideal domain specific agent modeling language would be tailored to a certain application domain (e.g. virtual worlds) as well as to the target execution environment (e.g. a legacy virtual reality platform). At the same time it is desirable ...
Slides
... But agents provide a more natural representation of real-world systems in which different individuals interact according to their own agendas and priorities. They can come together to achieve overarching objectives that might not, or not as easily, be achieved by the individuals alone ...
... But agents provide a more natural representation of real-world systems in which different individuals interact according to their own agendas and priorities. They can come together to achieve overarching objectives that might not, or not as easily, be achieved by the individuals alone ...
The extended BAM Neural Network Model
... memory (BAM) neural network model which can do auto- and hetero-associative memory. The theoretical proof for this neural network model’s stability is given. Experiments show that this neural network model is much more powerful than the M-P Model, Discrete Hopfield Neural Network, Continuous Hopfiel ...
... memory (BAM) neural network model which can do auto- and hetero-associative memory. The theoretical proof for this neural network model’s stability is given. Experiments show that this neural network model is much more powerful than the M-P Model, Discrete Hopfield Neural Network, Continuous Hopfiel ...
Learning in Markov Games with Incomplete Information
... The Markovgame (also called stochastic game (Filar & Vrieze 1997)) has been adopted as a theoretical frameworkfor multiagent reinforcement learning (Littman 1994). In a Markovgame, there are n agents, each facing a Markov decision process (MDP). All agents’ MDPsare correlated through their reward fu ...
... The Markovgame (also called stochastic game (Filar & Vrieze 1997)) has been adopted as a theoretical frameworkfor multiagent reinforcement learning (Littman 1994). In a Markovgame, there are n agents, each facing a Markov decision process (MDP). All agents’ MDPsare correlated through their reward fu ...
What is an agent?
... • To update the agent function in light of observed performance of percept-sequence to action pairs – Explore new parts of state space Learn from trial and error – Change internal variables that influence action selection ...
... • To update the agent function in light of observed performance of percept-sequence to action pairs – Explore new parts of state space Learn from trial and error – Change internal variables that influence action selection ...
Learning from learning curves: Item Response Theory
... Fit better when organized by knowledge components (productions) rather than surface forms (programming language terms) ...
... Fit better when organized by knowledge components (productions) rather than surface forms (programming language terms) ...
Introduction: What is AI?
... • Disease diagnosis (Quinlan’s ID3) – Database of patient information + disease state – Learns set of 3 simple rules, using 5 features to ...
... • Disease diagnosis (Quinlan’s ID3) – Database of patient information + disease state – Learns set of 3 simple rules, using 5 features to ...
Perspectives on System Identification
... System Identification is really ”System Approximation” and therefore closely related to Model Reduction. Model Reduction is a separate area with an extensive literature (``another satellite''), which can be more seriously linked to the system identification field. ...
... System Identification is really ”System Approximation” and therefore closely related to Model Reduction. Model Reduction is a separate area with an extensive literature (``another satellite''), which can be more seriously linked to the system identification field. ...
Page 113 - JUfiles
... 1. Autonomous agent–software agent that can adapt and alter the manner in which it attempts to achieve its assigned task. 2. Distributed agent–software agent that works on multiple distinct computer systems. 3. Mobile agent–software agent that can relocate itself onto different computer systems. 4. ...
... 1. Autonomous agent–software agent that can adapt and alter the manner in which it attempts to achieve its assigned task. 2. Distributed agent–software agent that works on multiple distinct computer systems. 3. Mobile agent–software agent that can relocate itself onto different computer systems. 4. ...
ItemResponseTheory - Carnegie Mellon School of Computer
... Fit better when organized by knowledge components (productions) rather than surface forms (programming language terms) ...
... Fit better when organized by knowledge components (productions) rather than surface forms (programming language terms) ...