Using Anytime Algorithms in Intelligent Systems
... Consider, for example, a speech-recognition system whose structure is shown in figure 4. Each box represents an elementary anytime algorithm whose conditional PP is given. The system is composed of three main components: First, the speaker is classified in terms of gender and accent. Then, a recogni ...
... Consider, for example, a speech-recognition system whose structure is shown in figure 4. Each box represents an elementary anytime algorithm whose conditional PP is given. The system is composed of three main components: First, the speaker is classified in terms of gender and accent. Then, a recogni ...
Potential Search: a Bounded
... solution is found. However, solutions with costs higher than C may be found first even though they are of no use. The main problem with all these variants is that the desired goal ...
... solution is found. However, solutions with costs higher than C may be found first even though they are of no use. The main problem with all these variants is that the desired goal ...
Neural coding of basic reward terms of animal
... learning reveals that rewards that are fully predicted do not contribute to learning [5]. Rather, the acquisition of associative strength of a conditioned stimulus depends on the discrepancy between the maximal associative strength sustained by the reinforcer and the current strength of the predicti ...
... learning reveals that rewards that are fully predicted do not contribute to learning [5]. Rather, the acquisition of associative strength of a conditioned stimulus depends on the discrepancy between the maximal associative strength sustained by the reinforcer and the current strength of the predicti ...
Multi-objective optimization of support vector machines
... Summary. Designing supervised learning systems is in general a multi-objective optimization problem. It requires finding appropriate trade-offs between several objectives, for example between model complexity and accuracy or sensitivity and specificity. We consider the adaptation of kernel and regul ...
... Summary. Designing supervised learning systems is in general a multi-objective optimization problem. It requires finding appropriate trade-offs between several objectives, for example between model complexity and accuracy or sensitivity and specificity. We consider the adaptation of kernel and regul ...
Heuristic Search
... To build as system to solve a particular problem, we need: Define the problem: must include precise specifications ~ initial solution & final solution. Analyze the problem: select the most important features that can have an immense impact. Isolate and represent : convert these important featu ...
... To build as system to solve a particular problem, we need: Define the problem: must include precise specifications ~ initial solution & final solution. Analyze the problem: select the most important features that can have an immense impact. Isolate and represent : convert these important featu ...
On the Sample Complexity of Reinforcement Learning with a Generative Model
... the above-mentioned gap between the lower bound and the upper bound, guarantee that no learning method, given the generative model of the MDP, can be significantly more efficient than QVI in terms of the sample complexity of estimating the action-value function. The main idea to improve the upper bo ...
... the above-mentioned gap between the lower bound and the upper bound, guarantee that no learning method, given the generative model of the MDP, can be significantly more efficient than QVI in terms of the sample complexity of estimating the action-value function. The main idea to improve the upper bo ...
The State of SAT - Cornell Computer Science
... that require exponentially large regular resolution proofs. Regular resolution proofs are DAGS, as in general resolution, but are restricted so that no variable is resolved upon more than once in any path from the root to a leaf. It is easy to see that all tree-like proofs are regular but not vice-v ...
... that require exponentially large regular resolution proofs. Regular resolution proofs are DAGS, as in general resolution, but are restricted so that no variable is resolved upon more than once in any path from the root to a leaf. It is easy to see that all tree-like proofs are regular but not vice-v ...
View Sample PDF
... processes, a clustering algorithm was developed (BenDor, Shamir, & Yakhini, 1999) that enabled detailed analysis of gene expression data. Recent advances in proteomics technologies, such as two-hybrid, phage display and mass spectrometry, have enabled the creation of detailed maps of biomolecular in ...
... processes, a clustering algorithm was developed (BenDor, Shamir, & Yakhini, 1999) that enabled detailed analysis of gene expression data. Recent advances in proteomics technologies, such as two-hybrid, phage display and mass spectrometry, have enabled the creation of detailed maps of biomolecular in ...
Ten Challenges Redux: Recent Progress in Propositional
... they showed that there are formulas with short clause learning proofs that require exponentially large regular resolution proofs. Regular resolution proofs are DAGS, as in general resolution, but are restricted so that no variable is resolved upon more than once in any path from the root to a leaf. ...
... they showed that there are formulas with short clause learning proofs that require exponentially large regular resolution proofs. Regular resolution proofs are DAGS, as in general resolution, but are restricted so that no variable is resolved upon more than once in any path from the root to a leaf. ...
A Comparative Illustration of AI Planning-based
... considering the fast growth of web services, building a full knowledge base by converting all web services into axioms, will be expensive. SWORD [Ponnekanti and Fox 2002] is an example of this approach. However, for more general WSC problem, often, AI planning based solutions such as STRIPS or Graph ...
... considering the fast growth of web services, building a full knowledge base by converting all web services into axioms, will be expensive. SWORD [Ponnekanti and Fox 2002] is an example of this approach. However, for more general WSC problem, often, AI planning based solutions such as STRIPS or Graph ...
Sets of Boolean Connectives that make Argumentation Easier
... support Φ without ϕ for α exactly if there exists a minimal such support); we will make this more precise in Section 3. We also mention that the problem of Arg-Rel is of particular importance, since it allows to determine (in terms of decision problems) the actual form of a potential support, an imp ...
... support Φ without ϕ for α exactly if there exists a minimal such support); we will make this more precise in Section 3. We also mention that the problem of Arg-Rel is of particular importance, since it allows to determine (in terms of decision problems) the actual form of a potential support, an imp ...
Problem formulation
... Solution: sb-down, sb-left, sb-up,sb-right, sb-down Path cost: 5 steps to reach the goal ...
... Solution: sb-down, sb-left, sb-up,sb-right, sb-down Path cost: 5 steps to reach the goal ...
Effective Constraint based Clustering Approach for Collaborative
... profiles are most similar to that of user u. Content-based methods use the features of items, e.g. movie’s genres, directors, actors, etc., to generate recommendations. Hybrid approaches [4] make recommendations by combining collaborative filtering and content-based recommendation. Collaborative Fil ...
... profiles are most similar to that of user u. Content-based methods use the features of items, e.g. movie’s genres, directors, actors, etc., to generate recommendations. Hybrid approaches [4] make recommendations by combining collaborative filtering and content-based recommendation. Collaborative Fil ...
NRC 39221
... for handling context-sensitive features in supervised machine learning from examples. We discuss two methods for recovering lost (implicit) contextual information. We mention some evidence that hybrid strategies can have a synergetic effect. We then show how the work of several machine learning rese ...
... for handling context-sensitive features in supervised machine learning from examples. We discuss two methods for recovering lost (implicit) contextual information. We mention some evidence that hybrid strategies can have a synergetic effect. We then show how the work of several machine learning rese ...
Real-Time Search for Autonomous Agents and
... Every real-time search algorithm always moves toward a state with the smallest estimated cost. As the lower bounds increase with learning, the estimated costs of visited states become higher than those of unvisited states. As a result, the algorithm tends to explore unvisited states, and often moves ...
... Every real-time search algorithm always moves toward a state with the smallest estimated cost. As the lower bounds increase with learning, the estimated costs of visited states become higher than those of unvisited states. As a result, the algorithm tends to explore unvisited states, and often moves ...
Learning Abductive Reasoning Using Random Examples
... programming (Denecker and Kakas 2002) approaches, the usual syntactic criteria, such as minimizing the number of literals as done in ATMS (Reiter and de Kleer 1987), appear to serve essentially a proxy for some other kind of unspecified domain-specific “plausibility” notion, by appealing to somethin ...
... programming (Denecker and Kakas 2002) approaches, the usual syntactic criteria, such as minimizing the number of literals as done in ATMS (Reiter and de Kleer 1987), appear to serve essentially a proxy for some other kind of unspecified domain-specific “plausibility” notion, by appealing to somethin ...
A WK-Means Approach for Clustering
... respectively. The most notable thing is that none of the other algorithms reach the worst solution found by the WK-means algorithm, even in their best solutions. At the same time, the standard deviation of solutions found by the WK-means algorithm is the smallest of all algorithms. This means that t ...
... respectively. The most notable thing is that none of the other algorithms reach the worst solution found by the WK-means algorithm, even in their best solutions. At the same time, the standard deviation of solutions found by the WK-means algorithm is the smallest of all algorithms. This means that t ...
PDF
... good progress in the area of Automated Reasoning. For example, SATO (Satisfiability Testing Optimized)[30], a model generator based on the Davis-PutnamLogemann-Loveland method for propositional clauses [7, 8], has solved a number of difficult open existence problems for Quasigroups with specific algebr ...
... good progress in the area of Automated Reasoning. For example, SATO (Satisfiability Testing Optimized)[30], a model generator based on the Davis-PutnamLogemann-Loveland method for propositional clauses [7, 8], has solved a number of difficult open existence problems for Quasigroups with specific algebr ...
22c:145 Artificial Intelligence
... Agent knows exactly which state it will be in. Solution is a sequence of actions. Non-observable environment =⇒ conformant problem Agent know it may be in any of a number of states. Solution, if any, is a sequence of actions. Nondeterministic and/or partially observable environment =⇒ contingency pr ...
... Agent knows exactly which state it will be in. Solution is a sequence of actions. Non-observable environment =⇒ conformant problem Agent know it may be in any of a number of states. Solution, if any, is a sequence of actions. Nondeterministic and/or partially observable environment =⇒ contingency pr ...
Word - Jim Davies
... s-image (not shown). Maps are created between the corresponding objects. In this example it would mean a map between ray1 in the left bottom s-image and the four rays in the second bottom s-image. This system does not solve the mapping problem, but a mapping from the correspondences of the first s-i ...
... s-image (not shown). Maps are created between the corresponding objects. In this example it would mean a map between ray1 in the left bottom s-image and the four rays in the second bottom s-image. This system does not solve the mapping problem, but a mapping from the correspondences of the first s-i ...
Dynamic traffic splitting to parallel wireless networks with partial information: a Bayesian approach
... part of the requested data in a separate TCP session. Previous work has indicated that downloading from multiple networks concurrently may not always be beneficial [12], but in general significant performance improvements can be realized [13, 17, 19]. Under these circumstances of using a combination ...
... part of the requested data in a separate TCP session. Previous work has indicated that downloading from multiple networks concurrently may not always be beneficial [12], but in general significant performance improvements can be realized [13, 17, 19]. Under these circumstances of using a combination ...
cs.cmu.edu - Stanford Artificial Intelligence Laboratory
... of histories is key to applying this approach to real robot problems. In this section we will compare different ways of limiting the number of histories maintained by the algorithm. For this purpose, we will use a problem (the Lady and the Tiger) which is small enough that we can maintain all possib ...
... of histories is key to applying this approach to real robot problems. In this section we will compare different ways of limiting the number of histories maintained by the algorithm. For this purpose, we will use a problem (the Lady and the Tiger) which is small enough that we can maintain all possib ...
Lesson 4 Slides-Classical and Advanced Techniques for Optimization
... Ant colony optimization (contd.) ...
... Ant colony optimization (contd.) ...
Exact Algorithms via Monotone Local Search
... constant c. We refer to the textbook of Fomin and Kratsch [20] for an introduction to the field. In the area of parameterized algorithms (see [12]), the goal is to design efficient algorithms for the “easy” instances of computationally intractable problems. Here the running time is measured not only ...
... constant c. We refer to the textbook of Fomin and Kratsch [20] for an introduction to the field. In the area of parameterized algorithms (see [12]), the goal is to design efficient algorithms for the “easy” instances of computationally intractable problems. Here the running time is measured not only ...
Rich Text Format - (QRG), Northwestern University
... How does an expert solve a problem? Looking at expert and novice differences in problem solving is one way of understanding the control knowledge of the expert. Larkin, McDermott, Simon and Simon (1980) have argued that in the domain of physics, novice problem solvers use backward inference while ex ...
... How does an expert solve a problem? Looking at expert and novice differences in problem solving is one way of understanding the control knowledge of the expert. Larkin, McDermott, Simon and Simon (1980) have argued that in the domain of physics, novice problem solvers use backward inference while ex ...
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.