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 ...
Some Approaches to Knowledge Acquisition
... knowledge source into a program. In our research, we have identified three different stages of knowledge acquisition and are examining different kinds of learning appropriate to each stage. We have given the chess labels of the “opening,” “middle game,” and “end game” to these stages (see Chapter 5 ...
... knowledge source into a program. In our research, we have identified three different stages of knowledge acquisition and are examining different kinds of learning appropriate to each stage. We have given the chess labels of the “opening,” “middle game,” and “end game” to these stages (see Chapter 5 ...
Cognition - Castle Wood School
... great capacity for changing (plasticity) through learning new things. Children’s memory capacity can be increased through use and this programme contains ideas for ways in which this can happen. Many children with SLD seem to have better long-term memories than working memories. They may need many r ...
... great capacity for changing (plasticity) through learning new things. Children’s memory capacity can be increased through use and this programme contains ideas for ways in which this can happen. Many children with SLD seem to have better long-term memories than working memories. They may need many r ...
The Arcade Learning Environment
... well in Atari 2600 games, much more work remains to be done. Different methods perform well on different games, and no single method performs best on all games. Some games, such as M ONTEZUMA’ S R EVENGE, are particularly challenging and require high-level planning far beyond what current domain-ind ...
... well in Atari 2600 games, much more work remains to be done. Different methods perform well on different games, and no single method performs best on all games. Some games, such as M ONTEZUMA’ S R EVENGE, are particularly challenging and require high-level planning far beyond what current domain-ind ...
Where is Education Heading and How About AI?
... different kinds of support so they can choose their own learning route and strategy adapted to their needs, capabilities and preferences. Educational technologists are seeking ways to create pedagogical tools that accommodate ideas of more encompassing environments that offer multiple didactic appro ...
... different kinds of support so they can choose their own learning route and strategy adapted to their needs, capabilities and preferences. Educational technologists are seeking ways to create pedagogical tools that accommodate ideas of more encompassing environments that offer multiple didactic appro ...
A Multistrategy Approach to Classifier Learning from Time
... called a convolutional code. Past values of a time series are stored by a particular type of recurrent ANN, which transforms the original data into its internal representation. This transformation can be formally defined in terms of a kernel function that is convolved over the time series. This defi ...
... called a convolutional code. Past values of a time series are stored by a particular type of recurrent ANN, which transforms the original data into its internal representation. This transformation can be formally defined in terms of a kernel function that is convolved over the time series. This defi ...
Artificial Intelligence 人工智能
... Too many hidden neurons : you get an over fit, training set is memorized, thus making the network useless on new data sets Not enough hidden neurons: network is unable to learn problem concept Too much examples, the ANN memorizes the examples instead of the general idea ...
... Too many hidden neurons : you get an over fit, training set is memorized, thus making the network useless on new data sets Not enough hidden neurons: network is unable to learn problem concept Too much examples, the ANN memorizes the examples instead of the general idea ...
Machine Learning Basics: 1. General Introduction
... Introduction to Machine Learning Machine Learning Basics: 1. General Introduction ...
... Introduction to Machine Learning Machine Learning Basics: 1. General Introduction ...
Artificial Intelligence in the Open World
... probabilistic and logic-based approaches that make decisions under limited time, I’ve often thought about Simon’s fanciful image of the ant interacting with the complex environment at the beach, wandering among the hills and nooks and crannies, in thinking about building computational intelligences ...
... probabilistic and logic-based approaches that make decisions under limited time, I’ve often thought about Simon’s fanciful image of the ant interacting with the complex environment at the beach, wandering among the hills and nooks and crannies, in thinking about building computational intelligences ...
AI*IA Workshop on Deep Understanding and Reasoning: A
... obsolete (if not in the spirit certainly in the details, as they have been immensely refined by successive research), but the problems and the issues raised are still up in their full glory. In particular two things come to my mind: the three criteria set forth by McCarthy and Hayes, and the problem ...
... obsolete (if not in the spirit certainly in the details, as they have been immensely refined by successive research), but the problems and the issues raised are still up in their full glory. In particular two things come to my mind: the three criteria set forth by McCarthy and Hayes, and the problem ...
Preface to UMUAI Special Issue on Machine Learning for User
... Chiu and Webb use decision tree learning for modeling subtraction skills. They present and compare a series of techniques for increasing the numbers of predictions made by the FBM-C4.5 modeling system. The models of the initial system take the form of a set of decision trees, where each tree makes p ...
... Chiu and Webb use decision tree learning for modeling subtraction skills. They present and compare a series of techniques for increasing the numbers of predictions made by the FBM-C4.5 modeling system. The models of the initial system take the form of a set of decision trees, where each tree makes p ...
Cognitive Requirements for Agent
... student to select a feedback option depending on the amount of structure, interaction, and feedback s/he desires when problem-solving. In this way the learneragent relationship becomes mutually collaborative as each provide feedback for each other. A related issue is in terms of how active the agent ...
... student to select a feedback option depending on the amount of structure, interaction, and feedback s/he desires when problem-solving. In this way the learneragent relationship becomes mutually collaborative as each provide feedback for each other. A related issue is in terms of how active the agent ...
18 LEARNING FROM OBSERVATIONS
... • A simple approach to deal with overfitting is to prune the decision tree • Pruning works by preventing recursive splitting on attributes that are not clearly relevant • Suppose we split a set of examples using an irrelevant attribute • Generally, we would expect the resulting subsets to have rough ...
... • A simple approach to deal with overfitting is to prune the decision tree • Pruning works by preventing recursive splitting on attributes that are not clearly relevant • Suppose we split a set of examples using an irrelevant attribute • Generally, we would expect the resulting subsets to have rough ...
Model-Centered Learning and Instruction
... of their experience or thought in such a way that they effect a systematic representation of this experience or thought, as a means of understanding it, or explaining it to others. In this article, the focus is on the psychological and epistemological aspects of model-centered learning and instructi ...
... of their experience or thought in such a way that they effect a systematic representation of this experience or thought, as a means of understanding it, or explaining it to others. In this article, the focus is on the psychological and epistemological aspects of model-centered learning and instructi ...
Position paper - SDDU
... stage in which the roles are played out, and the sets, scenery and props which help us act out or perform our roles convincingly. This is a rather more social psychological and individualistic account focusing on the representation of self rather than a more structural analysis which can be gained t ...
... stage in which the roles are played out, and the sets, scenery and props which help us act out or perform our roles convincingly. This is a rather more social psychological and individualistic account focusing on the representation of self rather than a more structural analysis which can be gained t ...
A conceptual model for game based intelligent tutoring
... good and bad answers. Textual responses from students were merely compared to the stored text answers using latent semantic analysis and results were fed into template tutor statements of positive, neutral or negative feedback, prompts for more information, elaboration, requestioning or summarising. ...
... good and bad answers. Textual responses from students were merely compared to the stored text answers using latent semantic analysis and results were fed into template tutor statements of positive, neutral or negative feedback, prompts for more information, elaboration, requestioning or summarising. ...
Reconceptualising outdoor adventure education
... that thinking can be studied as a sequential process of problem solving involving the manipulation of semantic or symbolic codes which represents objects or events (Holman et al., 1997). Thus, if a person does not appear to learn there is a problem with the individual (mental ability), or the wrong ...
... that thinking can be studied as a sequential process of problem solving involving the manipulation of semantic or symbolic codes which represents objects or events (Holman et al., 1997). Thus, if a person does not appear to learn there is a problem with the individual (mental ability), or the wrong ...
Project MLEXAI: applying machine learning to web document
... machine learning phase of the project consists of the following steps. Students load the ARFF files created in phase 2 and use Weka to explore several machine learning algorithms and their performance in automatic tagging of the web documents. For example, students use Weka’s decision tree algorithm ...
... machine learning phase of the project consists of the following steps. Students load the ARFF files created in phase 2 and use Weka to explore several machine learning algorithms and their performance in automatic tagging of the web documents. For example, students use Weka’s decision tree algorithm ...
lecture slides
... Bayesian co-training The standard GP inference on the previous slide can be used to predict function values given the observations. The equations are the same, but the kernel is now the co-training kernel. ● Note! The co-training kernel involves matrix inverses. It cannot be computed element-by-ele ...
... Bayesian co-training The standard GP inference on the previous slide can be used to predict function values given the observations. The equations are the same, but the kernel is now the co-training kernel. ● Note! The co-training kernel involves matrix inverses. It cannot be computed element-by-ele ...
Full size
... So far, most of the learning algorithms we’ve looked at have been supervised, passive, and offline. We’re given a labeled dataset and need to construct a hypothesis. Sometimes our agent is not that lucky. Placed in an environment and forced to learn the best action to take in each state. The environ ...
... So far, most of the learning algorithms we’ve looked at have been supervised, passive, and offline. We’re given a labeled dataset and need to construct a hypothesis. Sometimes our agent is not that lucky. Placed in an environment and forced to learn the best action to take in each state. The environ ...
New taxonomy of classification methods based on Formal Concepts
... relevant patterns that can be used for the classification step [1,13,12]. IPR13 is a method which introduces the coverage of concepts. It selects from the lattice all the relevant concepts which can help to better classification. The choice of relevant concepts is based on greedy algorithm [16]. The ...
... relevant patterns that can be used for the classification step [1,13,12]. IPR13 is a method which introduces the coverage of concepts. It selects from the lattice all the relevant concepts which can help to better classification. The choice of relevant concepts is based on greedy algorithm [16]. The ...
Michael Arbib and Laurent Itti: CS564
... Reinforcement Learning in Motor Control (Barto) * This week the HBTNN material is the required reading ...
... Reinforcement Learning in Motor Control (Barto) * This week the HBTNN material is the required reading ...