Exploiting Past and Future: Pruning by Inconsistent Partial State
... through their propagators, at least one value in the domain of one variable. We adapt this “proof-based” approach to extract an unsatisfiable core from any node of the search tree by incrementally collecting relevant information. Algorithm 1 depicts how to implement our method inside a backtracking ...
... through their propagators, at least one value in the domain of one variable. We adapt this “proof-based” approach to extract an unsatisfiable core from any node of the search tree by incrementally collecting relevant information. Algorithm 1 depicts how to implement our method inside a backtracking ...
Probability 101++ Hal Daumé III Computer Science University of Maryland
... In the running for most important AI equation! Hal Daumé III ([email protected]) ...
... In the running for most important AI equation! Hal Daumé III ([email protected]) ...
Artificial Intelligence 4. Knowledge Representation
... Why deriving the empty clause means a contradiction Why we negate the theorem statement Why proof by contradiction is valid Know that resolution has been applied to mathematics ...
... Why deriving the empty clause means a contradiction Why we negate the theorem statement Why proof by contradiction is valid Know that resolution has been applied to mathematics ...
Prof - University of Alberta
... Abstract. – We have chosen the language L11 in which to formulate the axioms of two systems of the linear Archimedean continua – the point-based system, SP, and the stretch-based system, SI – for the following reasons: 1. It enables us to formulate all the axioms of each system in one and the same ...
... Abstract. – We have chosen the language L11 in which to formulate the axioms of two systems of the linear Archimedean continua – the point-based system, SP, and the stretch-based system, SI – for the following reasons: 1. It enables us to formulate all the axioms of each system in one and the same ...
Learning Belief Networks in the Presence of Missing - CS
... datasets. For example, our procedure is able to learn structures in a domain with several dozen variables and 30% missing values, and to learn the structure in the presence of several hidden variables. (We note that in both of these experiments, we did not rely on prior knowledge to reduce the numbe ...
... datasets. For example, our procedure is able to learn structures in a domain with several dozen variables and 30% missing values, and to learn the structure in the presence of several hidden variables. (We note that in both of these experiments, we did not rely on prior knowledge to reduce the numbe ...
A Quotient Construction on Markov Chains with
... the time, they arise as discrete-time, finite-state, stationary Markov processes (i.e., Markov chains), which are fully determined by a transition matrix and an initial distribution. For example, in an earlier paper [14], we analyzed the dynamics of Young’s adaptive learning model in repeated games ...
... the time, they arise as discrete-time, finite-state, stationary Markov processes (i.e., Markov chains), which are fully determined by a transition matrix and an initial distribution. For example, in an earlier paper [14], we analyzed the dynamics of Young’s adaptive learning model in repeated games ...
Generative Adversarial Structured Networks
... To circumvent this issue, we can simply use the smoothed generator. Computing the marginals output by the smoothed generator is #P-hard in the general case, but it is tractable for certain models (e.g., chains and trees), and can be efficiently approximated using standard inference techniques, such ...
... To circumvent this issue, we can simply use the smoothed generator. Computing the marginals output by the smoothed generator is #P-hard in the general case, but it is tractable for certain models (e.g., chains and trees), and can be efficiently approximated using standard inference techniques, such ...
Practical Issues in Modeling Large Diagnostic Systems with Multiply
... be included in each d-sepset between subnets. The resultant representation will be a valid MSBN (assuming that other constraints are also satisfied). In practice, faults can form a spectrum from local to global. For instance, the overheating variable h in the above example may affect the three compo ...
... be included in each d-sepset between subnets. The resultant representation will be a valid MSBN (assuming that other constraints are also satisfied). In practice, faults can form a spectrum from local to global. For instance, the overheating variable h in the above example may affect the three compo ...
On Convergence Rate of a Class of Genetic Algorithms
... Above model covers many genetic algorithms. For example, the canonical GA [10] with binary coding is a special case of it. Furthermore, the canonical genetic algorithms [10] with elitist selection is also in this class. In this paper we consider the case that the population size is finite and denote ...
... Above model covers many genetic algorithms. For example, the canonical GA [10] with binary coding is a special case of it. Furthermore, the canonical genetic algorithms [10] with elitist selection is also in this class. In this paper we consider the case that the population size is finite and denote ...
Two Forms of Dependence in Propositional Logic
... be a countable set of propositional variables and P S the propositional language built up from PS , the connectives and the boolean constants true and false. For X PS , PROPX denotes the sublanguage of PROPP S generated from the variables of X only. Elements (resp. subsets) of PS are denoted x, y ...
... be a countable set of propositional variables and P S the propositional language built up from PS , the connectives and the boolean constants true and false. For X PS , PROPX denotes the sublanguage of PROPP S generated from the variables of X only. Elements (resp. subsets) of PS are denoted x, y ...
Combining satisfiability techniques from AI and OR
... to both communities, but until recently, the two fields have seldom collaborated. The fields have evolved independently, use different techniques, and each has a unique framework for approaching problems. It is only recently that there have been attempts to build algorithms integrating techniques fr ...
... to both communities, but until recently, the two fields have seldom collaborated. The fields have evolved independently, use different techniques, and each has a unique framework for approaching problems. It is only recently that there have been attempts to build algorithms integrating techniques fr ...
Description of the Distance Matrices
... Figure 1 shows an example image of the wing class. To extract a string representation out of such binary images, some preprocessing steps had to be done. First, edge detection was performed (Figure 2). Secondly, the edges were approximated by straight line segments of fixed length. Figure 3 shows th ...
... Figure 1 shows an example image of the wing class. To extract a string representation out of such binary images, some preprocessing steps had to be done. First, edge detection was performed (Figure 2). Secondly, the edges were approximated by straight line segments of fixed length. Figure 3 shows th ...
Structured Knowledge Representation and Schema Systems
... Structure Knowledge Representations were explored as a general representation for symbolic representation of declarative knowledge. One of the results was a theory for Schema Systems ...
... Structure Knowledge Representations were explored as a general representation for symbolic representation of declarative knowledge. One of the results was a theory for Schema Systems ...
Automated Endoscope Navigation and Advisory System from
... of the processing that will be performed on a series of images to extract all the identifiable features. The information is purely dependent on what can be extracted from the 'raw' images. At the signal level, the first task is performed by detecting a single dominant feature, lumen. Few methods of ...
... of the processing that will be performed on a series of images to extract all the identifiable features. The information is purely dependent on what can be extracted from the 'raw' images. At the signal level, the first task is performed by detecting a single dominant feature, lumen. Few methods of ...
Presentation
... a utility function in this case agent has probabilistic beliefs – pieces of knowledge with The Example: Adversarial search ...
... a utility function in this case agent has probabilistic beliefs – pieces of knowledge with The Example: Adversarial search ...
Full project report
... In order to solve CSP we can apply backtracking (BT) algorithm. BT is a general algorithm for finding all (or some) solutions to some computational problems, notably CSP, that incrementally builds candidates to the solutions, and abandons each partial candidate ("backtracks") as soon as it determine ...
... In order to solve CSP we can apply backtracking (BT) algorithm. BT is a general algorithm for finding all (or some) solutions to some computational problems, notably CSP, that incrementally builds candidates to the solutions, and abandons each partial candidate ("backtracks") as soon as it determine ...
Multi-Conditional Learning: Generative/Discriminative Training for
... a globally normalized product of local functions. In our experiments here we shall use the harmonium’s factorization structure to define an MRF and we will then define sets of marginal conditionals distributions of some observed variables given others that are of particular interest so as to form ou ...
... a globally normalized product of local functions. In our experiments here we shall use the harmonium’s factorization structure to define an MRF and we will then define sets of marginal conditionals distributions of some observed variables given others that are of particular interest so as to form ou ...
An Introduction to Probabilistic Graphical Models.
... A graphical model can be thought of as a probabilistic database, a machine that can answer “queries” regarding the values of sets of random variables. ...
... A graphical model can be thought of as a probabilistic database, a machine that can answer “queries” regarding the values of sets of random variables. ...
Non-CNF QBF Solving with QCIR - Institute for Formal Models and
... most state-of-the-art QBF solvers and therefore most applications which aim at using a QBF solver generate formulas in QDIMACS format. In order to establish non-CNF formats, several efforts have been taken. One of the first attempts was the boole2 format. It offers an infix representation which is v ...
... most state-of-the-art QBF solvers and therefore most applications which aim at using a QBF solver generate formulas in QDIMACS format. In order to establish non-CNF formats, several efforts have been taken. One of the first attempts was the boole2 format. It offers an infix representation which is v ...
w - Amazon S3
... We can express V and Q (approximately) as weighted linear functions of feature values: Vw(s) = w1f1(s) + w2f2(s) + … + wnfn(s) Qw(s,a) = w1f1(s,a) + w2f2(s,a) + … + wnfn(s,a) Important: depending on the features used, the best possible approximation may be terrible! But in practice we can ...
... We can express V and Q (approximately) as weighted linear functions of feature values: Vw(s) = w1f1(s) + w2f2(s) + … + wnfn(s) Qw(s,a) = w1f1(s,a) + w2f2(s,a) + … + wnfn(s,a) Important: depending on the features used, the best possible approximation may be terrible! But in practice we can ...
Generating New Beliefs From Old Fahiem Bacchus Adam J. Grove Joseph Y. Halpern
... the agent. Several ways of doing this have been considered in the literature; for example, [BGHK92, PV92] each discuss several possibilities. The approaches in [BGHK92] are based in a very natural way on the semantics described above. Assume we have a (prior) probability distribution over some set o ...
... the agent. Several ways of doing this have been considered in the literature; for example, [BGHK92, PV92] each discuss several possibilities. The approaches in [BGHK92] are based in a very natural way on the semantics described above. Assume we have a (prior) probability distribution over some set o ...
Learning Markov Networks With Arithmetic Circuits
... iteration, rather than rescoring every single one. A high-level view of our algorithm is shown in Algorithm 1. This simple description assumes that every split is rescored in every iteration. To achieve reasonable running times, our actual implementation of ACMN uses a priority queue which ranks spl ...
... iteration, rather than rescoring every single one. A high-level view of our algorithm is shown in Algorithm 1. This simple description assumes that every split is rescored in every iteration. To achieve reasonable running times, our actual implementation of ACMN uses a priority queue which ranks spl ...
Dempster-Shafer Theory
... Tossing a coin which is not known to be fair In both cases, we assign a probability of 0.5 to the proposition that the result is heads. In the first case this assignment is based on probabilistic knowledge, in the second case it is based on the absence of such knowledge. Generalizations of probabi ...
... Tossing a coin which is not known to be fair In both cases, we assign a probability of 0.5 to the proposition that the result is heads. In the first case this assignment is based on probabilistic knowledge, in the second case it is based on the absence of such knowledge. Generalizations of probabi ...
Solving Bayesian Networks by Weighted Model Counting
... Bacchus et al. 2003a), the creators of Cachet (Sang et al. 2004) built a system that scales to problems with thousands of variables by combining clause learning, formula-caching, and decomposition into connected components. Model-counting is complete for the complexity class #P, which also includes ...
... Bacchus et al. 2003a), the creators of Cachet (Sang et al. 2004) built a system that scales to problems with thousands of variables by combining clause learning, formula-caching, and decomposition into connected components. Model-counting is complete for the complexity class #P, which also includes ...