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 ...

novel sequence representations Reliable prediction of T

... and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance t ...

... and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance t ...

Handwritten Gregg Shorthand Recognition

... numbers of these basic components and the multiple connections between them. It also comes from genetic programming and learning. The individual neurons are complicated. They have a myriad of parts, sub-systems, and control mechanisms. They convey information via a host of electrochemical pathways. ...

... numbers of these basic components and the multiple connections between them. It also comes from genetic programming and learning. The individual neurons are complicated. They have a myriad of parts, sub-systems, and control mechanisms. They convey information via a host of electrochemical pathways. ...

Autonomous agent based on reinforcement learning

... recognizing the similarity of problem paradigms. The paper outlines a planning system which integrates the reinforcement learning method and a neural network approach with the aim to ensure autonomous robot behavior in unpredictable working conditions. The assumption is that the robot is a tabula ra ...

... recognizing the similarity of problem paradigms. The paper outlines a planning system which integrates the reinforcement learning method and a neural network approach with the aim to ensure autonomous robot behavior in unpredictable working conditions. The assumption is that the robot is a tabula ra ...

PNBA*: A Parallel Bidirectional Heuristic Search Algorithm

... search (candidates to be expanded). The closed list stores the nodes that have already been expanded. The open list helps the algorithm during the selection of the most promising node to expand and is commonly implemented as binary heap. The closed list structure is used by A* to avoid the expansion ...

... search (candidates to be expanded). The closed list stores the nodes that have already been expanded. The open list helps the algorithm during the selection of the most promising node to expand and is commonly implemented as binary heap. The closed list structure is used by A* to avoid the expansion ...

Reliable prediction of T-cell epitopes using neural networks with

... and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance t ...

... and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance t ...

preprint

... computations. Even the term ‘approximation’ appears to be often ill-defined. Take, for instance, again Bayesian abduction (computing the most probable explanation h out of a set of candidate explanations H, given a number of observations e, i.e., computing h such that Pr(H = h | e) is maximum). In t ...

... computations. Even the term ‘approximation’ appears to be often ill-defined. Take, for instance, again Bayesian abduction (computing the most probable explanation h out of a set of candidate explanations H, given a number of observations e, i.e., computing h such that Pr(H = h | e) is maximum). In t ...

artificial neural network circuit for spectral pattern recognition

... comes to speed. One of the circuits implemented in this thesis is plant disease classification using reflectance spectra. The ANN is trained to look at reflectance spectra of the leaves and decide if the leaves are healthy or diseased. This circuit, for example, has a good application in the real-wo ...

... comes to speed. One of the circuits implemented in this thesis is plant disease classification using reflectance spectra. The ANN is trained to look at reflectance spectra of the leaves and decide if the leaves are healthy or diseased. This circuit, for example, has a good application in the real-wo ...

The Even More Irresistible SROIQ

... We describe an extension, called SROIQ, of the description logic (DL) SHOIN (14) underlying OWL-DL (9).1 SHOIN can be said to provide most expressive means that one could reasonably expect from the logical basis of an ontology language, and to constitute a good compromise between expressive power an ...

... We describe an extension, called SROIQ, of the description logic (DL) SHOIN (14) underlying OWL-DL (9).1 SHOIN can be said to provide most expressive means that one could reasonably expect from the logical basis of an ontology language, and to constitute a good compromise between expressive power an ...

Prediction of lower extremities` movement by angle–angle diagrams

... Furthermore, we could study these characteristics of cyclograms: the length of trajectory, frequency of loops, slope of loops, maximum range, average speed, total circumscribed area of loops. The most important aim of our work was to design the methods for applying the cyclograms in practice to iden ...

... Furthermore, we could study these characteristics of cyclograms: the length of trajectory, frequency of loops, slope of loops, maximum range, average speed, total circumscribed area of loops. The most important aim of our work was to design the methods for applying the cyclograms in practice to iden ...

Compiling Proof Search in Semantic Tableaux - IT-SEC

... conventional programming languages by defining a function for each node that recursively calls functions for successor nodes after assigning a corresponding truth value to the atom of the node, if possible. Practical experiments with C and 8086 Assembler have shown, that Prolog programs of the above ...

... conventional programming languages by defining a function for each node that recursively calls functions for successor nodes after assigning a corresponding truth value to the atom of the node, if possible. Practical experiments with C and 8086 Assembler have shown, that Prolog programs of the above ...

Visualizing Inference Henry Lieberman and Joe Henke MIT Media Lab

... Graphical visualization has demonstrated enormous power in helping people to understand complexity in many branches of science. But, curiously, AI has been slow to pick up on the power of visualization. Alar is a visualization system intended to help people understand and control symbolic inference. ...

... Graphical visualization has demonstrated enormous power in helping people to understand complexity in many branches of science. But, curiously, AI has been slow to pick up on the power of visualization. Alar is a visualization system intended to help people understand and control symbolic inference. ...

deep variational bayes filters: unsupervised learning of state space

... a mathematically correct lower bound to the marginal data likelihood. More significantly, their recognition model requires all observations that contain information w.r.t. the current state. This is nothing short of an additional temporal i.i.d. assumption on data: Multiple raw samples need to be st ...

... a mathematically correct lower bound to the marginal data likelihood. More significantly, their recognition model requires all observations that contain information w.r.t. the current state. This is nothing short of an additional temporal i.i.d. assumption on data: Multiple raw samples need to be st ...

Querying Data Graphs with Arithmetical Regular Expressions

... the elements of p is in a given interval. L ARE allows for using nested queries and their negation. This facilitates formulation of properties such as there is a one-way path from s to t, i.e. a path in which for any two consecutive nodes v, v ′ , there is no path from v ′ to v. Properties like thes ...

... the elements of p is in a given interval. L ARE allows for using nested queries and their negation. This facilitates formulation of properties such as there is a one-way path from s to t, i.e. a path in which for any two consecutive nodes v, v ′ , there is no path from v ′ to v. Properties like thes ...

Fuzzy Logic and Neural Nets

... • We have three things sets we have reason to believe we are in, and each set covers a range of values • Two options in going from current state to a single value: – Mean of Max: Take the rule we believe most strongly, and take the (weighted) average of its possible values – Center of Mass: Take all ...

... • We have three things sets we have reason to believe we are in, and each set covers a range of values • Two options in going from current state to a single value: – Mean of Max: Take the rule we believe most strongly, and take the (weighted) average of its possible values – Center of Mass: Take all ...

From Certainty Factors to Belief Networks

... research community has largely abandoned the use of CFs. In our laboratory, where the CF model was originally developed, we have not used CFs in our systems for over a decade. We accordingly welcome the opportunity to review the CF model, the reasons for its creation, and recent developments and ana ...

... research community has largely abandoned the use of CFs. In our laboratory, where the CF model was originally developed, we have not used CFs in our systems for over a decade. We accordingly welcome the opportunity to review the CF model, the reasons for its creation, and recent developments and ana ...

Diagrammatic Representation and Reasoning: Some Distinctions

... seem to involve these properties. Motion is potentially part of all three: diagrams can use animation, and visual and spatial representations can involve sequences of images. There seems to be much more of a general agreement, at least on an intuitive level, on the notion of diagrammatic representat ...

... seem to involve these properties. Motion is potentially part of all three: diagrams can use animation, and visual and spatial representations can involve sequences of images. There seems to be much more of a general agreement, at least on an intuitive level, on the notion of diagrammatic representat ...

AAAI Proceedings Template

... about temporal aspects in pixel patterns that can be observed as beliefs by higher-order NSCAs to reason about these hypotheses. This effectively creates a layered or deep belief network, which is more robust to changes in environmental conditions (e.g. lighting, camera position, type of objects). ...

... about temporal aspects in pixel patterns that can be observed as beliefs by higher-order NSCAs to reason about these hypotheses. This effectively creates a layered or deep belief network, which is more robust to changes in environmental conditions (e.g. lighting, camera position, type of objects). ...

Generative Adversarial Structured Networks

... The generative adversarial learning paradigm has significantly advanced the field of unsupervised learning. The adversarial framework pits a generator against a discriminator in a non-cooperative two-player game: the generator’s goal is to generate artificial samples that are convincing enough to be ...

... The generative adversarial learning paradigm has significantly advanced the field of unsupervised learning. The adversarial framework pits a generator against a discriminator in a non-cooperative two-player game: the generator’s goal is to generate artificial samples that are convincing enough to be ...

Probabilistic graphical models in artificial intelligence

... followed by other expert systems and some of these created their own method for handling uncertainty with rules and methods mainly based on intuition. This was the case of INTERNIST-1 [6]. Some of these suffered from important inconsistencies, mainly due to the non-distinction between absolute and u ...

... followed by other expert systems and some of these created their own method for handling uncertainty with rules and methods mainly based on intuition. This was the case of INTERNIST-1 [6]. Some of these suffered from important inconsistencies, mainly due to the non-distinction between absolute and u ...

Conflict-Based Search For Optimal Multi

... 2004). However, efficiently building such heuristics for general MAPF algorithms is an open question. ...

... 2004). However, efficiently building such heuristics for general MAPF algorithms is an open question. ...

Using Semantic Cues to Learn Syntax

... or left and v is the valence of the parent. Valence encodes how many children have been generated by the parent before generating the current child. It can take one of the three values: 0, 1 or 2. A value of 2 indicates that the parent already has two or more children. This component of the model is ...

... or left and v is the valence of the parent. Valence encodes how many children have been generated by the parent before generating the current child. It can take one of the three values: 0, 1 or 2. A value of 2 indicates that the parent already has two or more children. This component of the model is ...

ppt - CSE, IIT Bombay

... M* and influences it , while being influenced by M*. Even one mental representation works as a network. In this sense MR does not stand for other (external) objects. It operates by means of the direct causal properties of M and M* and employs feed back loop and forward loop Any theoretical framework ...

... M* and influences it , while being influenced by M*. Even one mental representation works as a network. In this sense MR does not stand for other (external) objects. It operates by means of the direct causal properties of M and M* and employs feed back loop and forward loop Any theoretical framework ...

Here - Institute of Cognitive Neuroscience

... over which different representations are maintained, the uses they are put to, and how they interact with each other. However, there is currently no clear consensus, with various investigators stressing one or the other type of representation (e.g., cf. Poucet, 1993; Wang & Spelke, 2002). To address ...

... over which different representations are maintained, the uses they are put to, and how they interact with each other. However, there is currently no clear consensus, with various investigators stressing one or the other type of representation (e.g., cf. Poucet, 1993; Wang & Spelke, 2002). To address ...

Reasoning about Time

... reasoning techniques to deal specifically with temporal constraints between temporal entities (time points or intervals), independently of the events and states associated with them. For instance, given three time intervals I1 , I2 and I3 , if I1 is before I2 and I2 is before I3 , then one can infer ...

... reasoning techniques to deal specifically with temporal constraints between temporal entities (time points or intervals), independently of the events and states associated with them. For instance, given three time intervals I1 , I2 and I3 , if I1 is before I2 and I2 is before I3 , then one can infer ...

# Hierarchical temporal memory

Hierarchical temporal memory (HTM) is an online machine learning model developed by Jeff Hawkins and Dileep George of Numenta, Inc. that models some of the structural and algorithmic properties of the neocortex. HTM is a biomimetic model based on the memory-prediction theory of brain function described by Jeff Hawkins in his book On Intelligence. HTM is a method for discovering and inferring the high-level causes of observed input patterns and sequences, thus building an increasingly complex model of the world.Jeff Hawkins states that HTM does not present any new idea or theory, but combines existing ideas to mimic the neocortex with a simple design that provides a large range of capabilities. HTM combines and extends approaches used in Sparse distributed memory, Bayesian networks, spatial and temporal clustering algorithms, while using a tree-shaped hierarchy of nodes that is common in neural networks.