draft pdf
... on highly structured and loopy formulas. To exploit this information even further, we extend the framework borrowed from SampleCount with the use of biased random coins during randomized value selection for variables. The model count can, in fact, also be estimated directly from just one fixed point ...
... on highly structured and loopy formulas. To exploit this information even further, we extend the framework borrowed from SampleCount with the use of biased random coins during randomized value selection for variables. The model count can, in fact, also be estimated directly from just one fixed point ...
Problem Difficulty and the Phase Transition in Heuristic Search
... and p, we generated 1000 random instances. For each instance, we record whether or not a solution was found and the number of nodes expanded in order to find a solution or prove that no solution exists. We present results only for n = 100000 as the other plots show the same behavior. The Phase Trans ...
... and p, we generated 1000 random instances. For each instance, we record whether or not a solution was found and the number of nodes expanded in order to find a solution or prove that no solution exists. We present results only for n = 100000 as the other plots show the same behavior. The Phase Trans ...
New approaches for heuristic search: linkage with artificial
... network methods, simulated annealing is nevertheless compatible with them, and has been proposed as one possible adjunct to these methods as a way of enhancing their performance. The name "simulated annealing" derives from the intent to pattern the approach after the physical process of annealing, w ...
... network methods, simulated annealing is nevertheless compatible with them, and has been proposed as one possible adjunct to these methods as a way of enhancing their performance. The name "simulated annealing" derives from the intent to pattern the approach after the physical process of annealing, w ...
PDF
... preferable to an action that leads to the same outcome but with only 10% chance. What about a choice between actions that lead to different rewards? When comparing the worth of qualitatively different outcomes such as food and water, the motivational state of the animal must come into consideration ...
... preferable to an action that leads to the same outcome but with only 10% chance. What about a choice between actions that lead to different rewards? When comparing the worth of qualitatively different outcomes such as food and water, the motivational state of the animal must come into consideration ...
AIRS: Anytime Iterative Refinement of a Solution
... In this paper, we use two versions of the A* algorithm: Weighted A* (WA*) for the initial solution and Bidirectional A* (BidA*) for the refinement algorithm. Recall that A* (Hart, Nilsson, and Raphael 1968; 1972) defines the state evaluation function f = g + h, with includes the accumulated distance a ...
... In this paper, we use two versions of the A* algorithm: Weighted A* (WA*) for the initial solution and Bidirectional A* (BidA*) for the refinement algorithm. Recall that A* (Hart, Nilsson, and Raphael 1968; 1972) defines the state evaluation function f = g + h, with includes the accumulated distance a ...
Chapter 2
... reason for this is because the domain expert finds it very difficult to express tacit knowledge in terms that can be used to solve the specific problem. Finally, Luger and Stubblefield (2002) conclude that the most widely used knowledge representation scheme for expert systems is rule-based. Normall ...
... reason for this is because the domain expert finds it very difficult to express tacit knowledge in terms that can be used to solve the specific problem. Finally, Luger and Stubblefield (2002) conclude that the most widely used knowledge representation scheme for expert systems is rule-based. Normall ...
approximate reasoning using anytime algorithms
... implementation. However, the use of anytime algorithms as the components of a modular system presents a special type of scheduling problem. The question is how much time to allocate to each component in order to maximize the output quality of the complete system. We refer to this problem as the anyt ...
... implementation. However, the use of anytime algorithms as the components of a modular system presents a special type of scheduling problem. The question is how much time to allocate to each component in order to maximize the output quality of the complete system. We refer to this problem as the anyt ...
Learning Optimal Bayesian Networks Using A
... The basic idea of our algorithm is to formulate learning optimal Bayesian networks as a shortest path finding problem. We use the order graph in Figure 1 as the search graph. We let the top-most node that contains no variables be the start state and the bottom-most node with all variables be the goa ...
... The basic idea of our algorithm is to formulate learning optimal Bayesian networks as a shortest path finding problem. We use the order graph in Figure 1 as the search graph. We let the top-most node that contains no variables be the start state and the bottom-most node with all variables be the goa ...
University of Michigan Jerusalem, Israel durfee/
... can employ various methods, including iterated dominance and adopting assumptions about the agents and solving for optimal mixed strategies. Different analyses might use different solution concepts, and thus (as seen above) different decisions can be rational in the context of different solution con ...
... can employ various methods, including iterated dominance and adopting assumptions about the agents and solving for optimal mixed strategies. Different analyses might use different solution concepts, and thus (as seen above) different decisions can be rational in the context of different solution con ...
From: AAAI- Proceedings. Copyright © , AAAI (www.aaai.org). All rights reserved. 97
... Most analyses of heuristic evaluation functions relate the performance of a search algorithm to the accuracy of the heuristic as an estimate of the exact distance to the goal. One difficulty with this approach is that heuristic accuracy is hard to measure, since determining the exact distance to th ...
... Most analyses of heuristic evaluation functions relate the performance of a search algorithm to the accuracy of the heuristic as an estimate of the exact distance to the goal. One difficulty with this approach is that heuristic accuracy is hard to measure, since determining the exact distance to th ...
Easy Problems are Sometimes Hard
... 0.2. The dotted line in these graphs indicates the probability of satisfiability. The dashed line represents the median, and the solid line the mean, of the number of branches searched, 3 both plotted using the same logarithmic scale. The median graph shows the standard easy-hard-easy pattern as the ...
... 0.2. The dotted line in these graphs indicates the probability of satisfiability. The dashed line represents the median, and the solid line the mean, of the number of branches searched, 3 both plotted using the same logarithmic scale. The median graph shows the standard easy-hard-easy pattern as the ...
The Model-based Approach to Autonomous Behavior: A
... MDP and POMDP models are also useful when the uncertainty in the system dynamics or in the feedback is represented by means of sets rather than probability distributions. Planning with a dynamics and feedback represented in this qualitative manner is called contingent planning. Solutions to these mo ...
... MDP and POMDP models are also useful when the uncertainty in the system dynamics or in the feedback is represented by means of sets rather than probability distributions. Planning with a dynamics and feedback represented in this qualitative manner is called contingent planning. Solutions to these mo ...
Impact of Participants` Market Power and Transmission Constraints
... through strategic biddings. This paper investigates the problem of developing optimal bidding strategies of GenCos considering participants’ market power and transmission constraints. The problem is modeled as a bi-level optimization that at the first level each GenCo maximizes its payoff through st ...
... through strategic biddings. This paper investigates the problem of developing optimal bidding strategies of GenCos considering participants’ market power and transmission constraints. The problem is modeled as a bi-level optimization that at the first level each GenCo maximizes its payoff through st ...
Algorithm Selection for Combinatorial Search Problems: A Survey
... decisions can be made and the effect of a bad choice of algorithm is potentially less severe. The price for this added flexibility is a higher overhead, as algorithms need to be selected more frequently. Both approaches have been used from the very start of research into algorithm selection. The cho ...
... decisions can be made and the effect of a bad choice of algorithm is potentially less severe. The price for this added flexibility is a higher overhead, as algorithms need to be selected more frequently. Both approaches have been used from the very start of research into algorithm selection. The cho ...
Semantics and derivation for Stochastic Logic Programs
... from the SLP in descending order of their probability according to either the Normalised or NF interpretation of (note that the order imposed by probabilities in these two interpretations is identical). In many applications, such as diagnosis, it is expected that such a descending enumeration would ...
... from the SLP in descending order of their probability according to either the Normalised or NF interpretation of (note that the order imposed by probabilities in these two interpretations is identical). In many applications, such as diagnosis, it is expected that such a descending enumeration would ...
Advanced Research into AI Ising Computer (PDF format, 212KB)
... spin states (σi), the interaction coefficients (Jij) that represent the strength of the interactions between different pairs of spin states, and the external magnetic coefficients (hi) that represent the strength of the external magnetic field. The figure also includes the equation for the energy (H ...
... spin states (σi), the interaction coefficients (Jij) that represent the strength of the interactions between different pairs of spin states, and the external magnetic coefficients (hi) that represent the strength of the external magnetic field. The figure also includes the equation for the energy (H ...
Um Provador de Teoremas Multi-Estratégia A Multi
... be able to produce KE systems. On the implementation side, from the results we have obtained3 , we can see that it would be very useful to have an adaptive strategy that changes its behavior according to features of the problem presented to it. This strategy can behave as other implemented strategie ...
... be able to produce KE systems. On the implementation side, from the results we have obtained3 , we can see that it would be very useful to have an adaptive strategy that changes its behavior according to features of the problem presented to it. This strategy can behave as other implemented strategie ...
Learning to Plan in Complex Stochastic Domains
... our algorithm intended to traverse the edge between state u and v, with some non-zero probability the environment may instead transition to a different state, w. Consequently, the problem of probabilistic planning is significantly more difficult than deterministic planning, but allows for more accur ...
... our algorithm intended to traverse the edge between state u and v, with some non-zero probability the environment may instead transition to a different state, w. Consequently, the problem of probabilistic planning is significantly more difficult than deterministic planning, but allows for more accur ...
On the Sample Complexity of Reinforcement Learning with a Generative Model
... on the sample complexity of the QVI algorithm. The new upper bound improves on the existing bound of QVI by an order of 1/(1 − γ). We also present a new minimax lower bound of Θ N log(N/δ)/ (1−γ)3 ε2 , which also improves on the best existing lower bound of RL by an order of 1/(1 − γ). The new re ...
... on the sample complexity of the QVI algorithm. The new upper bound improves on the existing bound of QVI by an order of 1/(1 − γ). We also present a new minimax lower bound of Θ N log(N/δ)/ (1−γ)3 ε2 , which also improves on the best existing lower bound of RL by an order of 1/(1 − γ). The new re ...
approximate reasoning using anytime algorithms
... implementation. However, the use of anytime algorithms as the components of a modular system presents a special type of scheduling problem. The question is how much time to allocate to each component in order to maximize the output quality of the complete system. We refer to this problem as the anyt ...
... implementation. However, the use of anytime algorithms as the components of a modular system presents a special type of scheduling problem. The question is how much time to allocate to each component in order to maximize the output quality of the complete system. We refer to this problem as the anyt ...
penultimate version PDF - METU Department of Philosophy
... psychic given or a datum of a mental state. On the contrary, it is an embodiment in which the subject and his surrounding environment should be situated in an agentive relation. Therefore, agency is primary, even in defining objectivity. Reasoning and intelligence are not located in the organism; th ...
... psychic given or a datum of a mental state. On the contrary, it is an embodiment in which the subject and his surrounding environment should be situated in an agentive relation. Therefore, agency is primary, even in defining objectivity. Reasoning and intelligence are not located in the organism; th ...
Probably Approximately Correct Heuristic Search
... to generate accurate heuristics have also been proposed (Samadi, Felner, and Schaeffer 2008; Jabbari Arfaee, Zilles, and Holte 2010). The resulting heuristics are not guaranteed to be admissible but were shown to be very effective. No theoretical analysis of the amount of suboptimality was performed ...
... to generate accurate heuristics have also been proposed (Samadi, Felner, and Schaeffer 2008; Jabbari Arfaee, Zilles, and Holte 2010). The resulting heuristics are not guaranteed to be admissible but were shown to be very effective. No theoretical analysis of the amount of suboptimality was performed ...
Markov Chains
... We illustrate Theorem 11.12 by writing a program to simulate the behavior of a Markov chain. SimulateChain is such a program. Example 11.21 In the Land of Oz, there are 525 days in a year. We have simulated the weather for one year in the Land of Oz, using the program SimulateChain. The results are ...
... We illustrate Theorem 11.12 by writing a program to simulate the behavior of a Markov chain. SimulateChain is such a program. Example 11.21 In the Land of Oz, there are 525 days in a year. We have simulated the weather for one year in the Land of Oz, using the program SimulateChain. The results are ...
Pattern Recognition Algorithms for Cluster
... with associated probabilities, for some value of N, instead of simply a single best label. When the number of possible labels is fairly small (e.g. in the case of classification), N may be set so that the probability of all possible labels is output. Probabilistic algorithms have many advantages ove ...
... with associated probabilities, for some value of N, instead of simply a single best label. When the number of possible labels is fairly small (e.g. in the case of classification), N may be set so that the probability of all possible labels is output. Probabilistic algorithms have many advantages ove ...
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.