Towards the Theory-Guided Design of Help
... be useless to one person and helpful to another? Existing intelligent tutorial and help systems have not always provided satisfactory answers to such questions. For example, the information delivered to the learner may assume too little or too much knowledge, or the user interaction is too restricti ...
... be useless to one person and helpful to another? Existing intelligent tutorial and help systems have not always provided satisfactory answers to such questions. For example, the information delivered to the learner may assume too little or too much knowledge, or the user interaction is too restricti ...
Probabilistic Planning via Determinization in Hindsight
... give a class of planning problems for which HOP is guaranteed to be optimal (when focusing solely on success ratio). Here we focus our analysis on the objective of maximizing goal achievement probability. In this case, R(s, F, π) is always either 1 or 0 depending on whether π reached the goal in the ...
... give a class of planning problems for which HOP is guaranteed to be optimal (when focusing solely on success ratio). Here we focus our analysis on the objective of maximizing goal achievement probability. In this case, R(s, F, π) is always either 1 or 0 depending on whether π reached the goal in the ...
Application of Artificial Intelligence in Finance
... part of the ES technology, and was not given much attention in the past. However, it is now widely accepted that the user interface can make a critical difference in the perceived utility of a system regardless of the systems. ...
... part of the ES technology, and was not given much attention in the past. However, it is now widely accepted that the user interface can make a critical difference in the perceived utility of a system regardless of the systems. ...
Incremental temporal reasoning in job shop scheduling repair Please share
... logic and neural network [2]. This paper will aim at constraint-based scheduling approaches because (1) the real-time status of job shop could be collected by RFID devices and manipulated by a reactive monitoring system, which is built on a constraint-based reactive language (reactive model-based pr ...
... logic and neural network [2]. This paper will aim at constraint-based scheduling approaches because (1) the real-time status of job shop could be collected by RFID devices and manipulated by a reactive monitoring system, which is built on a constraint-based reactive language (reactive model-based pr ...
Forward and Backward Chaining and and
... It’s only true that “X is savings” if “savings are inadequate”. That provides a subgoal under “X is savings” It’s only true that “X is stocks” if “savings are adequate” and “income is adequate. That provides two subgoals under “X is stocks” joined by “and” links. Similarly, there are two subgoal ...
... It’s only true that “X is savings” if “savings are inadequate”. That provides a subgoal under “X is savings” It’s only true that “X is stocks” if “savings are adequate” and “income is adequate. That provides two subgoals under “X is stocks” joined by “and” links. Similarly, there are two subgoal ...
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... knowledge-directed information passing task could require a set of logical rules to characterize prop erty inheritance relationships. Hypothesis matching might involve inferences based on object-part rela tionships, causal relationships, geometric relation ships, or temporal relationships. Since ...
... knowledge-directed information passing task could require a set of logical rules to characterize prop erty inheritance relationships. Hypothesis matching might involve inferences based on object-part rela tionships, causal relationships, geometric relation ships, or temporal relationships. Since ...
Improving Reinforcement Learning by using Case Based
... The applicability of the solution of the case depends on the position of the opponents, and combine two functions: the free path function, which considers if the trajectory of the ball indicated in the case is free of opponents, in order for the evaluated case to be applicable and the opponent simil ...
... The applicability of the solution of the case depends on the position of the opponents, and combine two functions: the free path function, which considers if the trajectory of the ball indicated in the case is free of opponents, in order for the evaluated case to be applicable and the opponent simil ...
ARM and Machine Learning
... Machine Learning is having a significant impact on ARM’s roadmap for future processors and architectures ...
... Machine Learning is having a significant impact on ARM’s roadmap for future processors and architectures ...
An algorithm for inducing least generalization under relative
... Inductive Logic Programming (ILP) investigates the problem of inducing clausal theories from given sets of positive and negative examples. An inductively inferred theory must imply all of the positive examples and none of the negative examples. The problem to find the least generalization of a set o ...
... Inductive Logic Programming (ILP) investigates the problem of inducing clausal theories from given sets of positive and negative examples. An inductively inferred theory must imply all of the positive examples and none of the negative examples. The problem to find the least generalization of a set o ...
Improving Learning Performance Through Rational ... Jonathan Gratch*, Steve Chien+, and ...
... gorithms ignore these factors when making their selection. This section discusses the relevant factors and shows that they can be folded into a single value, the disparity index. We show that in theory an algorithm can achieve large performance improvements by exploiting this information, if only it ...
... gorithms ignore these factors when making their selection. This section discusses the relevant factors and shows that they can be folded into a single value, the disparity index. We show that in theory an algorithm can achieve large performance improvements by exploiting this information, if only it ...
Efficient Classification of Multi-label and Imbalanced Data Using Min
... the module size becomes smaller, especially for the small classes. This means that there are more true positives in the result. On the other hand, the precision value of each class decreases as the module size gets smaller, with the exception of those classes that cannot be predicted. In other words ...
... the module size becomes smaller, especially for the small classes. This means that there are more true positives in the result. On the other hand, the precision value of each class decreases as the module size gets smaller, with the exception of those classes that cannot be predicted. In other words ...
Heuristic search in artificial intelligence
... that the new planners are competitive with some of the best current planners. Hansen and Zilberstein consider searching for optimal policies to solve problems modeled as Markov decision processes, a common framework used in AI for planning and learning under uncertainty. Their new search algorithm, ...
... that the new planners are competitive with some of the best current planners. Hansen and Zilberstein consider searching for optimal policies to solve problems modeled as Markov decision processes, a common framework used in AI for planning and learning under uncertainty. Their new search algorithm, ...
CS 188: Artificial Intelligence Example: Grid World Recap: MDPs
... Alternative approach for optimal values: Step 1: Policy evaluation: calculate utilities for some fixed policy (not optimal utilities!) until convergence Step 2: Policy improvement: update policy using one‐step look‐ahead with resulting converged (but not optimal!) utilities as future values ...
... Alternative approach for optimal values: Step 1: Policy evaluation: calculate utilities for some fixed policy (not optimal utilities!) until convergence Step 2: Policy improvement: update policy using one‐step look‐ahead with resulting converged (but not optimal!) utilities as future values ...
An Ensemble Method for Clustering
... assigned to the same cluster is the construction of a pairwise concordance matrix (Fred, 2001). This matrix can then be used as a distance matrix for a hierarchical clustering algorithm. The main problem of such an approach is the size of such ...
... assigned to the same cluster is the construction of a pairwise concordance matrix (Fred, 2001). This matrix can then be used as a distance matrix for a hierarchical clustering algorithm. The main problem of such an approach is the size of such ...
1994-Learning to Coordinate without Sharing Information
... trends. The first curve in the plot represents thefunction corresponding to the number of updates required to reach steady state value (with 6 = 6). The second curve represents the average number of trials required for a run to converge, scaled down by a constant factor of 0.06. The actual ratios be ...
... trends. The first curve in the plot represents thefunction corresponding to the number of updates required to reach steady state value (with 6 = 6). The second curve represents the average number of trials required for a run to converge, scaled down by a constant factor of 0.06. The actual ratios be ...
Finding the M Most Probable Configurations using Loopy Belief
... Loopy belief propagation (BP) has been successfully used in a number of difficult graphical models to find the most probable configuration of the hidden variables. In applications ranging from protein folding to image analysis one would like to find not just the best configuration but rather the top ...
... Loopy belief propagation (BP) has been successfully used in a number of difficult graphical models to find the most probable configuration of the hidden variables. In applications ranging from protein folding to image analysis one would like to find not just the best configuration but rather the top ...
Chapter 5 - Dr. Djamel Bouchaffra
... Info Availability & Solution Quality – Costs are associated with activities of an agent solution quality – You have to trade off computation time and solution quality: an anytime algorithm can provide a solution at any time; given more time it can produce better solutions (e.g., hill-climbing algo ...
... Info Availability & Solution Quality – Costs are associated with activities of an agent solution quality – You have to trade off computation time and solution quality: an anytime algorithm can provide a solution at any time; given more time it can produce better solutions (e.g., hill-climbing algo ...
Model-based Overlapping Clustering
... The overlapping clustering model that we present here is a generalization of the SBK model described in Section 2. The SBK model minimizes the squared loss between X and MA, and their proposed algorithms is not applicable for estimating the optimal M and A corresponding to other loss functions. In M ...
... The overlapping clustering model that we present here is a generalization of the SBK model described in Section 2. The SBK model minimizes the squared loss between X and MA, and their proposed algorithms is not applicable for estimating the optimal M and A corresponding to other loss functions. In M ...
Winner determination in combinatorial auctions using hybrid ant
... Winner Determination Problem (WDP) in combinatorial auctions is the problem of finding winning bids that maximize the auctioneer’s revenue under constraint, where each item can be allocated to at most one bidder. WDP is known as an NP-hard problem with practical applications like electronic commerce ...
... Winner Determination Problem (WDP) in combinatorial auctions is the problem of finding winning bids that maximize the auctioneer’s revenue under constraint, where each item can be allocated to at most one bidder. WDP is known as an NP-hard problem with practical applications like electronic commerce ...
Probabilistic Reasoning and the Design of Expert Systems
... 2.1 A Brief Introduction to the Bayesian Approach To this point in our presentation we have ignored an important component of expert system work. That is, in many applications the if… then… rules of the system are NOT always certain “if and only if” or cause/effect relationships. That is, many of th ...
... 2.1 A Brief Introduction to the Bayesian Approach To this point in our presentation we have ignored an important component of expert system work. That is, in many applications the if… then… rules of the system are NOT always certain “if and only if” or cause/effect relationships. That is, many of th ...
S - melnikov.info
... O. Maimon and L. Rokach. Introduction to supervised methods, Data Mining and Knowledge ...
... O. Maimon and L. Rokach. Introduction to supervised methods, Data Mining and Knowledge ...
CAHOOTS: A SOFTWARE PLATFORM FOR ENHANCING
... In our view, the next generation of group innovation processes needs to be grounded in a visualization method that is intuitive to use and robustly models the task of problem solving (i.e. ergosemantic). In this way, the new tool should be doubly adapted (Clark, 1997), both to the user and the task ...
... In our view, the next generation of group innovation processes needs to be grounded in a visualization method that is intuitive to use and robustly models the task of problem solving (i.e. ergosemantic). In this way, the new tool should be doubly adapted (Clark, 1997), both to the user and the task ...
Implementation of hybrid software architecture for Artificial
... as a whole. The Artificial Intelligence systems can be broadly classified into reactive systems, deliberative systems and interacting systems. Over the past, numerous architectures have been proposed in the literature for these systems addressing the important features of these systems. Reactive sys ...
... as a whole. The Artificial Intelligence systems can be broadly classified into reactive systems, deliberative systems and interacting systems. Over the past, numerous architectures have been proposed in the literature for these systems addressing the important features of these systems. Reactive sys ...
Turing Test as a Defining Feature of AI-Completeness
... way of showing other problems to be AI-Complete. We can either show that a problem is both in the set of AI problems and all other AI problem can be converted into it by some polynomial time algorithm or we can reduce any instance of Turing Test problem (or any other already proven to be AI-Complete ...
... way of showing other problems to be AI-Complete. We can either show that a problem is both in the set of AI problems and all other AI problem can be converted into it by some polynomial time algorithm or we can reduce any instance of Turing Test problem (or any other already proven to be AI-Complete ...
Multi-armed bandit
In probability theory, the multi-armed bandit problem (sometimes called the K- or N-armed bandit problem) is a problem in which a gambler at a row of slot machines (sometimes known as ""one-armed bandits"") has to decide which machines to play, how many times to play each machine and in which order to play them. When played, each machine provides a random reward from a distribution specific to that machine. The objective of the gambler is to maximize the sum of rewards earned through a sequence of lever pulls.Robbins in 1952, realizing the importance of the problem, constructed convergent population selection strategies in ""some aspects of the sequential design of experiments"".A theorem, the Gittins index published first by John C. Gittins gives an optimal policy in the Markov setting for maximizing the expected discounted reward.In practice, multi-armed bandits have been used to model the problem of managing research projects in a large organization, like a science foundation or a pharmaceutical company. Given a fixed budget, the problem is to allocate resources among the competing projects, whose properties are only partially known at the time of allocation, but which may become better understood as time passes.In early versions of the multi-armed bandit problem, the gambler has no initial knowledge about the machines. The crucial tradeoff the gambler faces at each trial is between ""exploitation"" of the machine that has the highest expected payoff and ""exploration"" to get more information about the expected payoffs of the other machines. The trade-off between exploration and exploitation is also faced in reinforcement learning.