Subspace Clustering, Ensemble Clustering, Alternative Clustering
... on new data). However, a strong bias may also hinder the representation of a good model of the true laws of nature one would like to learn. A weighted sum of hypotheses may then expand the space of possible models. To improve over several self-contained classifiers by building an ensemble of those c ...
... on new data). However, a strong bias may also hinder the representation of a good model of the true laws of nature one would like to learn. A weighted sum of hypotheses may then expand the space of possible models. To improve over several self-contained classifiers by building an ensemble of those c ...
Automated Negotiations Among Autonomous Agents
... Distributed software systems are a norm in today’s computing environment. These systems typically comprise of many autonomous components that interact with each other and negotiate to accomplish joint tasks. Today, we can integrate potentially disparate components such that they act coherently by co ...
... Distributed software systems are a norm in today’s computing environment. These systems typically comprise of many autonomous components that interact with each other and negotiate to accomplish joint tasks. Today, we can integrate potentially disparate components such that they act coherently by co ...
Psychopharmacology of conditioned reward
... 1938). The acquisition of a new response with a conditioned stimulus as the only reinforcement demonstrates the rewarding properties of that stimulus and has become an accepted criterion for identifying stimuli as conditioned rewards (Mackintosh 1974). The third procedure utilizes second-order sched ...
... 1938). The acquisition of a new response with a conditioned stimulus as the only reinforcement demonstrates the rewarding properties of that stimulus and has become an accepted criterion for identifying stimuli as conditioned rewards (Mackintosh 1974). The third procedure utilizes second-order sched ...
Case Representation Issues for Case
... This theorem was proposed by the Marquis of Condorcet in 1784 (Condorcet, 1784) – a more accessible reference is (Nitzan & Paroush, 1985). We know now that M will be greater that p only if there is diversity in the pool of voters. And we know that the probability of the ensemble being correct will ...
... This theorem was proposed by the Marquis of Condorcet in 1784 (Condorcet, 1784) – a more accessible reference is (Nitzan & Paroush, 1985). We know now that M will be greater that p only if there is diversity in the pool of voters. And we know that the probability of the ensemble being correct will ...
fulltext
... This thesis considers computer-assisted troubleshooting of heavy vehicles such as trucks and buses. In this setting, the person that is troubleshooting a vehicle problem is assisted by a computer that is capable of listing possible faults that can explain the problem and gives recommendations of whi ...
... This thesis considers computer-assisted troubleshooting of heavy vehicles such as trucks and buses. In this setting, the person that is troubleshooting a vehicle problem is assisted by a computer that is capable of listing possible faults that can explain the problem and gives recommendations of whi ...
Constraint Programming: In Pursuit of the Holy Grail
... very simple and non-efficient they are important because they make the foundation of more advanced and efficient algorithms. The basic constraint satisfaction algorithm, that searches the space of complete labellings, is called generate-and-test (GT). The idea of GT is simple: first, a complete labe ...
... very simple and non-efficient they are important because they make the foundation of more advanced and efficient algorithms. The basic constraint satisfaction algorithm, that searches the space of complete labellings, is called generate-and-test (GT). The idea of GT is simple: first, a complete labe ...
Using a Goal-Agenda and Committed Actions in Real
... one action π = ha1 , . . . , an i to a state s is recursively defined as γ(s, ha1 , . . . , an i) = γ(γ(s, ha1 , . . . , an−1 i), han i). Applying an empty plan does nothing, i.e., γ(s, hi) = s. A planning problem (A, s0 , g) is a triplet where A is the set of actions, s0 the initial state and g the ...
... one action π = ha1 , . . . , an i to a state s is recursively defined as γ(s, ha1 , . . . , an i) = γ(γ(s, ha1 , . . . , an−1 i), han i). Applying an empty plan does nothing, i.e., γ(s, hi) = s. A planning problem (A, s0 , g) is a triplet where A is the set of actions, s0 the initial state and g the ...
Artificial Intelligence Search Algorithms In Travel Planning
... Brute force or blind search is a uniformed exploration of the search space and it does not explicitly take into account either planning efficiency or execution efficiency. Blind search is also called uniform search. It is the search which has no information about its domain [3]. The only thing that ...
... Brute force or blind search is a uniformed exploration of the search space and it does not explicitly take into account either planning efficiency or execution efficiency. Blind search is also called uniform search. It is the search which has no information about its domain [3]. The only thing that ...
NEURAL ACTIVITY RELATED TO ANTICIPATED REWARD:
... species, extending from pigeons to humans, value judgments are subject to time-discounting. A reward of a given size is perceived as having greater or lesser value according to whether delivery is anticipated after a shorter or longer delay (Cardinal et al. 2001; Evenden and Ryan 1996; Herrnstein 1 ...
... species, extending from pigeons to humans, value judgments are subject to time-discounting. A reward of a given size is perceived as having greater or lesser value according to whether delivery is anticipated after a shorter or longer delay (Cardinal et al. 2001; Evenden and Ryan 1996; Herrnstein 1 ...
Disregarding Duration Uncertainty in Partial Order - LIA
... been added to prevent the occurrence of resource conflicts, whatever the activity durations are. A POS can be obtained through a variety of methods (the reader may refer for details to [7, 4, 5, 10–13]). A POS can be designed to optimize some probabilistic performance metric, such as the expected ma ...
... been added to prevent the occurrence of resource conflicts, whatever the activity durations are. A POS can be obtained through a variety of methods (the reader may refer for details to [7, 4, 5, 10–13]). A POS can be designed to optimize some probabilistic performance metric, such as the expected ma ...
Artificial Intelligence
... all living species are intelligent. But how about these plants and tress, they are living species but are they also intelligent? So can we say that Intelligence is a trait of some living species? Let us try to understand the phenomena of intelligence by using a few examples. Consider the following i ...
... all living species are intelligent. But how about these plants and tress, they are living species but are they also intelligent? So can we say that Intelligence is a trait of some living species? Let us try to understand the phenomena of intelligence by using a few examples. Consider the following i ...
... work. What has not been generally noticed is that different researchers have often applied the term to rather different aspects of their programs. Things that would be called a heuristic by one researcher would not be so called by others. This is because many heuristics embody a variety of different ...
What is a heuristic? - University of Alberta
... work. What has not been generally noticed is that different researchers have often applied the term to rather different aspects of their programs. Things that would be called a heuristic by one researcher would not be so called by others. This is because many heuristics embody a variety of different ...
... work. What has not been generally noticed is that different researchers have often applied the term to rather different aspects of their programs. Things that would be called a heuristic by one researcher would not be so called by others. This is because many heuristics embody a variety of different ...
Variational Inference for Dirichlet Process Mixtures
... The normalizing constants for these conditional distributions are assumed to be available analytically for settings in which Gibbs sampling is appropriate. Variational inference is based on reformulating the problem of computing the posterior distribution as an optimization problem, perturbing (or, ...
... The normalizing constants for these conditional distributions are assumed to be available analytically for settings in which Gibbs sampling is appropriate. Variational inference is based on reformulating the problem of computing the posterior distribution as an optimization problem, perturbing (or, ...
Morphine effects on monetary reward - DUO
... rewards. In humans, opioid agonists can induce euphoria, whereas antagonists reduce food reward. Brain regions implicated in reward processing such as the mesolimbic reward system are rich in µ-opioid receptors. We investigated the role of the µ-opioid receptor system in human reward processing usin ...
... rewards. In humans, opioid agonists can induce euphoria, whereas antagonists reduce food reward. Brain regions implicated in reward processing such as the mesolimbic reward system are rich in µ-opioid receptors. We investigated the role of the µ-opioid receptor system in human reward processing usin ...
Kobayashi S, Kawagoe R, Takikawa Y, Koizumi M, Sakagami M
... extent, by the currently expected reward value. For example, when the reinforcement ratio is different for each choice position, choice behavior is biased to a position where reward is expected with a higher probability (Herrnstein 1961). The basal ganglia have been suggested to play an important ro ...
... extent, by the currently expected reward value. For example, when the reinforcement ratio is different for each choice position, choice behavior is biased to a position where reward is expected with a higher probability (Herrnstein 1961). The basal ganglia have been suggested to play an important ro ...
Determining if Two Documents are by the Same Author
... The Internet is replete with documents that are written pseudonymously or anonymously and it is often of considerable financial or legal importance to determine if two such documents were in fact written by a single author. For example, we might want to know if several tendentious product reviews we ...
... The Internet is replete with documents that are written pseudonymously or anonymously and it is often of considerable financial or legal importance to determine if two such documents were in fact written by a single author. For example, we might want to know if several tendentious product reviews we ...
Amoeba-Based Emergent Computing: Combinatorial Optimization
... circular route A → B → C → D → A is one of the shortest (optimal) solutions, whereas A → C → B → D → A and A → C → D → B → A are the second-shortest and the longest solutions, respectively. TSP is a particularly hard problem among typical combinatorial optimization problems [25]. Because the number ...
... circular route A → B → C → D → A is one of the shortest (optimal) solutions, whereas A → C → B → D → A and A → C → D → B → A are the second-shortest and the longest solutions, respectively. TSP is a particularly hard problem among typical combinatorial optimization problems [25]. Because the number ...
An Introduction to Variational Methods for Graphical Models
... Even in cases in which the complexity of the exact algorithms is manageable, there can be reason to consider approximation procedures. Note in particular that the exact algorithms make no use of the numerical representation of the joint probability distribution associated with a graphical model; put ...
... Even in cases in which the complexity of the exact algorithms is manageable, there can be reason to consider approximation procedures. Note in particular that the exact algorithms make no use of the numerical representation of the joint probability distribution associated with a graphical model; put ...
Perspectives on Artificial Intelligence Planning
... time. More precisely, we assume a duration z[&{a|x for each } action , and a predicate @i~1\A that defines when a set of actions can be executed concurrently. For example, in the presence of unary resources, @i~1\A will be false if contains a pair of actions using the same res ...
... time. More precisely, we assume a duration z[&{a|x for each } action , and a predicate @i~1\A that defines when a set of actions can be executed concurrently. For example, in the presence of unary resources, @i~1\A will be false if contains a pair of actions using the same res ...
A Classification of Hyper-heuristic Approaches
... without learning. Hyper-heuristics without learning include approaches that use several heuristics (neighbourhood structures), but select the heuristics to call according to a predetermined sequence. Therefore, this category contains approaches such as variable neighbourhood search [42]. The hyper-h ...
... without learning. Hyper-heuristics without learning include approaches that use several heuristics (neighbourhood structures), but select the heuristics to call according to a predetermined sequence. Therefore, this category contains approaches such as variable neighbourhood search [42]. The hyper-h ...
Subset Selection of Search Heuristics
... 2009], regressors [Ernandes and Gori, 2004], and metric embeddings [Rayner et al., 2011], each capable of generating multiple different heuristic functions based on input parameters. When multiple heuristics are available, it is common to query each and somehow combine the resulting values into a be ...
... 2009], regressors [Ernandes and Gori, 2004], and metric embeddings [Rayner et al., 2011], each capable of generating multiple different heuristic functions based on input parameters. When multiple heuristics are available, it is common to query each and somehow combine the resulting values into a be ...
Universal Artificial Intelligence
... • The subjectivist uses probabilities to characterize an agent’s degree of belief in something, rather than to characterize physical random processes. • This is the most relevant interpretation of probabilities in AI. • We define the plausibility of an event as the degree of belief in the event, or ...
... • The subjectivist uses probabilities to characterize an agent’s degree of belief in something, rather than to characterize physical random processes. • This is the most relevant interpretation of probabilities in AI. • We define the plausibility of an event as the degree of belief in the event, or ...
Introducing Preferences in Planning as Satisfiability
... (in the context of CP-net [7]) and then in [13, 33] in the SAT setting, is to explore the search space of possible plans in accordance with the preferences expressed as a partial order, i.e. to force the splitting of the SAT solver in order to follow the given partial order on preferences. Qualitati ...
... (in the context of CP-net [7]) and then in [13, 33] in the SAT setting, is to explore the search space of possible plans in accordance with the preferences expressed as a partial order, i.e. to force the splitting of the SAT solver in order to follow the given partial order on preferences. Qualitati ...
Heuristics - UCLA Cognitive Systems Laboratory
... Defined in either way, a stochastic shortest-path problem is a special case of a fully-observable infinite-horizon Markov decision process (MDP). There are several ...
... Defined in either way, a stochastic shortest-path problem is a special case of a fully-observable infinite-horizon Markov decision process (MDP). There are several ...
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.