Poster - The University of Manchester
... IAMB (Incremental Association Markov Blanket) is an algorithm for finding the Markov Blanket of a target node in a Bayesian Network. I The Markov Blanket is the set of all parents, children and co-parents of a node. I The algorithm proceeds by using a conditional independence test to add nodes which ...
... IAMB (Incremental Association Markov Blanket) is an algorithm for finding the Markov Blanket of a target node in a Bayesian Network. I The Markov Blanket is the set of all parents, children and co-parents of a node. I The algorithm proceeds by using a conditional independence test to add nodes which ...
Adaptive Fuzzy Clustering of Data With Gaps
... Partition Coefficient (PC) - measures the amount of "overlapping" between cluster. The optimal number of cluster is at the maximum value. Classification Entropy (CE) - it measures the fuzzyness of the cluster partition only, which is similar to the Partition Coeffcient. Partition Index (SC) - ...
... Partition Coefficient (PC) - measures the amount of "overlapping" between cluster. The optimal number of cluster is at the maximum value. Classification Entropy (CE) - it measures the fuzzyness of the cluster partition only, which is similar to the Partition Coeffcient. Partition Index (SC) - ...
Exploiting Bounds in Operations Research and Artificial Intelligence
... in various disciplines and areas [2, 25, 24]. Even though most of the difficult optimization problems belong to the category of NP-hard [18], which is a worst-case complexity measure, in practice, a BnB algorithm can indeed very often alleviate computational difficulties and make large and complex p ...
... in various disciplines and areas [2, 25, 24]. Even though most of the difficult optimization problems belong to the category of NP-hard [18], which is a worst-case complexity measure, in practice, a BnB algorithm can indeed very often alleviate computational difficulties and make large and complex p ...
Localized Satisfiability For Multi-Context Systems
... is called a local model of Li . Interpretations of an entire MCS are called chains. They are constructed from sets of local models. Definition 2 (Chain) A chain c over a set of indices I is a sequence {ci }i∈I , where each ci ⊆ Mi is a set of local models of Li . A chain c is i-consistent if ci is n ...
... is called a local model of Li . Interpretations of an entire MCS are called chains. They are constructed from sets of local models. Definition 2 (Chain) A chain c over a set of indices I is a sequence {ci }i∈I , where each ci ⊆ Mi is a set of local models of Li . A chain c is i-consistent if ci is n ...
Quadratic Assignment Problem and its Relevance to the Real World
... Section 6 briefly concludes the survey and gives the future research directions regarding QAP. ...
... Section 6 briefly concludes the survey and gives the future research directions regarding QAP. ...
astic Strategy act ead
... agent and discuss the impact of deliberation overhead on its performance.. In a contracting situation, an agent often faces many factors and tradeoffs. To find the best payment to offer, for example, a contractor needs to think about the potential contractees’ costs of doing the task, the payments o ...
... agent and discuss the impact of deliberation overhead on its performance.. In a contracting situation, an agent often faces many factors and tradeoffs. To find the best payment to offer, for example, a contractor needs to think about the potential contractees’ costs of doing the task, the payments o ...
Bellman Equations Value Estimates Value Iteration
... converge if every state is visited infinitely often In fact, we can update the policy as seldom or often as we like, and we will still converge ...
... converge if every state is visited infinitely often In fact, we can update the policy as seldom or often as we like, and we will still converge ...
Project specification
... possible sudoku puzzle are in the order of 6*10^21 and if only unique solutions are considered (considering relabelling, reflections, rotations etc), there are approximately 5*10^9 solutions. Since the problem that will be studied is an unfinished sudoku puzzle with a unique solution it is more intr ...
... possible sudoku puzzle are in the order of 6*10^21 and if only unique solutions are considered (considering relabelling, reflections, rotations etc), there are approximately 5*10^9 solutions. Since the problem that will be studied is an unfinished sudoku puzzle with a unique solution it is more intr ...
An information-theoretic approach to curiosity
... randomness. As a result, exploration emerges as a directed behavior that optimizes information gain, rather than being modeled solely as behavior randomization. ...
... randomness. As a result, exploration emerges as a directed behavior that optimizes information gain, rather than being modeled solely as behavior randomization. ...
Operational Rationality through Compilation of Anytime Algorithms
... the compilation of functional composition is shown to be NP complete in the strong sense. However, local compilation techniques, whose complexity is linear in the size of the program, are shown to be both efficient and optimal for a large class of programs. In addition, a number of approximate time- ...
... the compilation of functional composition is shown to be NP complete in the strong sense. However, local compilation techniques, whose complexity is linear in the size of the program, are shown to be both efficient and optimal for a large class of programs. In addition, a number of approximate time- ...
PowerPoint Presentation - Computing Science
... Each frame has a number of slots. Each slot can be assigned a slot value. ...
... Each frame has a number of slots. Each slot can be assigned a slot value. ...
Dynamic Restart Policies - Association for the Advancement of
... Proposition 1 The optimal restart policy for a mixed runtime distribution with independent runs and no additional observations is the optimal fixed cutoff restart policy for the combined distribution. It is more interesting, therefore, to consider situations where the system can make observations th ...
... Proposition 1 The optimal restart policy for a mixed runtime distribution with independent runs and no additional observations is the optimal fixed cutoff restart policy for the combined distribution. It is more interesting, therefore, to consider situations where the system can make observations th ...
Dynamic Restart Policies
... Proposition 1 The optimal restart policy for a mixed runtime distribution with independent runs and no additional observations is the optimal fixed cutoff restart policy for the combined distribution. It is more interesting, therefore, to consider situations where the system can make observations th ...
... Proposition 1 The optimal restart policy for a mixed runtime distribution with independent runs and no additional observations is the optimal fixed cutoff restart policy for the combined distribution. It is more interesting, therefore, to consider situations where the system can make observations th ...
Bayesian Methods in Artificial Intelligence
... Many problems involve reasoning about a probabilistic system that changes with time, for example when controlling a mobile robot. The uncertainty in such system can be caused by noisy sensors or a complex dynamic environment. Bayesian models described in the previous chapter can be extended to be ap ...
... Many problems involve reasoning about a probabilistic system that changes with time, for example when controlling a mobile robot. The uncertainty in such system can be caused by noisy sensors or a complex dynamic environment. Bayesian models described in the previous chapter can be extended to be ap ...
Scheduling Contract Algorithms on Multiple Processors
... Chassaing (1999) consider the case where the performance profile is known and the deadline is drawn from a known distribution. In this case, the problem of scheduling a contract algorithm on a single processor to maximize the expected quality of results at the deadline can be framed as a Markov deci ...
... Chassaing (1999) consider the case where the performance profile is known and the deadline is drawn from a known distribution. In this case, the problem of scheduling a contract algorithm on a single processor to maximize the expected quality of results at the deadline can be framed as a Markov deci ...
Planning for Agents with Changing Goals
... with is the specification of the goals that must be achieved in a given task. This goal specification may consist of the actual goals to be achieved, as well as the values of achieving such goals, and priorities and deadlines (if any) associated with these goals. The fact that the system’s goals are ...
... with is the specification of the goals that must be achieved in a given task. This goal specification may consist of the actual goals to be achieved, as well as the values of achieving such goals, and priorities and deadlines (if any) associated with these goals. The fact that the system’s goals are ...
AI Research in the 21st Century
... and interactions in the body, of people, societies, industries, economies, stock markets, brains, minds, or human languages is impossible. These are “Bizarre” problem domains – defined as domains where models cannot be created or used and are discussed in detail at http://artificial-intuition.com. R ...
... and interactions in the body, of people, societies, industries, economies, stock markets, brains, minds, or human languages is impossible. These are “Bizarre” problem domains – defined as domains where models cannot be created or used and are discussed in detail at http://artificial-intuition.com. R ...
Parallel Processing, Part 1
... When it is impossible or difficult to decompose a large problem into sub problems with equal solution times, one might use random decisions that lead to good results with very high probability. Example: sorting with random sampling Approximation Iterative numerical methods may use approximation to ...
... When it is impossible or difficult to decompose a large problem into sub problems with equal solution times, one might use random decisions that lead to good results with very high probability. Example: sorting with random sampling Approximation Iterative numerical methods may use approximation to ...
Genetic Algorithms and Evolution - Centre for Pattern Analysis
... mutation rate to evolve at the same rate as the variables in the chromosome. Another area of focus in the past decade is the development of multi-objective evolutionary algorithms. Most of the implementations have been based upon Genetic Algorithms but a few have been implemented using Evolution St ...
... mutation rate to evolve at the same rate as the variables in the chromosome. Another area of focus in the past decade is the development of multi-objective evolutionary algorithms. Most of the implementations have been based upon Genetic Algorithms but a few have been implemented using Evolution St ...
INTELLIGENT AGENT PLANNING WITH QUASI
... presently extensive in real-life situations, such as deciding the optimal treatment of patients, classifying electronic payments as legitimate or not, speech and writing recognition, etc. All these and similar ones could be part of a longer plan of an agent. Another advantage of using learning for q ...
... presently extensive in real-life situations, such as deciding the optimal treatment of patients, classifying electronic payments as legitimate or not, speech and writing recognition, etc. All these and similar ones could be part of a longer plan of an agent. Another advantage of using learning for q ...
The Minimum k-Colored Subgraph Problem in
... In this paper we consider the minimum k-colored subgraph problem (MkCSP), which is motivated by maximum parsimony based population haplotyping and minimum primer set selection for DNA amplification by multiplex Polymerase Chain Reaction, two important problems in computational biology. We use severa ...
... In this paper we consider the minimum k-colored subgraph problem (MkCSP), which is motivated by maximum parsimony based population haplotyping and minimum primer set selection for DNA amplification by multiplex Polymerase Chain Reaction, two important problems in computational biology. We use severa ...
Improved Memory-Bounded Dynamic Programming for
... state during execution time, it can be used to evaluate the bottom-up policy trees computed by the DP algorithm. A set of belief states can be computed using multiple top-down heuristics – efficient algorithms that find useful top-down policies. Once a top-down heuristic policy is generated, the mos ...
... state during execution time, it can be used to evaluate the bottom-up policy trees computed by the DP algorithm. A set of belief states can be computed using multiple top-down heuristics – efficient algorithms that find useful top-down policies. Once a top-down heuristic policy is generated, the mos ...
Agent Shell for the Development of Tutoring Systems for Expert
... Fig. 1. Learning and Tutoring Agent Shell of the concept of expert system shell [10]. The problem solving engines of our LTAS employ a general, divide-and-conquer, approach to problem solving, called problem-reduction/solution-synthesis, which is applicable in a wide range of domains [7], [11]. In t ...
... Fig. 1. Learning and Tutoring Agent Shell of the concept of expert system shell [10]. The problem solving engines of our LTAS employ a general, divide-and-conquer, approach to problem solving, called problem-reduction/solution-synthesis, which is applicable in a wide range of domains [7], [11]. In t ...
Privacy Preserving Bayes-Adaptive MDPs
... [7] Abe, N., Verma, N., Apte, C., and Schroko, R., Cross channel optimized marketing by reinforcement learning, ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining, 2004 [8] Cogill, R., Rotkowitz, M., Van Roy, B., and Lall, S., An Approximate Dynamic Programming Approach to Decentralized C ...
... [7] Abe, N., Verma, N., Apte, C., and Schroko, R., Cross channel optimized marketing by reinforcement learning, ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining, 2004 [8] Cogill, R., Rotkowitz, M., Van Roy, B., and Lall, S., An Approximate Dynamic Programming Approach to Decentralized C ...
Introduction to Artificial Intelligence and Soft Computing
... that state from one or more known starting (or goal) states. For example, consider the 4-puzzle problem, where the goal state is known and one has to identify the moves for reaching the goal from a pre-defined starting state. Now, the less number of states one generates for reaching the goal, the be ...
... that state from one or more known starting (or goal) states. For example, consider the 4-puzzle problem, where the goal state is known and one has to identify the moves for reaching the goal from a pre-defined starting state. Now, the less number of states one generates for reaching the goal, the be ...
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.