Ontologies and Knowledge Representation Outline - (CUI)
... Axiomatized ontology: distinguishes subtypes by axioms and definitions stated in a formal language, (logic or some computeroriented notation that can be translated to logic) ...
... Axiomatized ontology: distinguishes subtypes by axioms and definitions stated in a formal language, (logic or some computeroriented notation that can be translated to logic) ...
Statistical Causal Inference
... causal graph is also assumed to be complete in the sense that all of the causal relations among the specified variables are included in the graph. For example, the graph in Fig. 4 has no edge from Y to S, so it is only accurate if the level of nicotine stains does not in any way cause smoking behav ...
... causal graph is also assumed to be complete in the sense that all of the causal relations among the specified variables are included in the graph. For example, the graph in Fig. 4 has no edge from Y to S, so it is only accurate if the level of nicotine stains does not in any way cause smoking behav ...
Logics for Collective Reasoning
... A fundamental intuition concerning logic is due to the tradition of substructural logics and in particular to Jean Yves Girard’s Linear Logic [5]: The structural rules of the sequent calculus determine the behavior of logical connectives. For instance, classical logic is imposed, by assuming weakeni ...
... A fundamental intuition concerning logic is due to the tradition of substructural logics and in particular to Jean Yves Girard’s Linear Logic [5]: The structural rules of the sequent calculus determine the behavior of logical connectives. For instance, classical logic is imposed, by assuming weakeni ...
A Partial Taxonomy of Substitutability and Interchangeability
... In this section, we review the various forms of interchangeability originally introduced in [Freuder, 1991]. We also include a few new interchangeability concepts that directly relate to the original ones. Full interchangeability, the most basic form of interchangeability, is defined as follows. Ful ...
... In this section, we review the various forms of interchangeability originally introduced in [Freuder, 1991]. We also include a few new interchangeability concepts that directly relate to the original ones. Full interchangeability, the most basic form of interchangeability, is defined as follows. Ful ...
On the realization of asymmetric high radix signed digital
... synaptic strengths of biological neurons. In both cases, some inputs are made more important than others so that they have a greater effect on the processing element as they combine to produce a neural response. Component 2.Summation Function: The first step in a processing element's operation is to ...
... synaptic strengths of biological neurons. In both cases, some inputs are made more important than others so that they have a greater effect on the processing element as they combine to produce a neural response. Component 2.Summation Function: The first step in a processing element's operation is to ...
On the Relationship Between Sum-Product Networks and Bayesian
... probability theory to compactly model complex, real-world phenomena. Many commonly used statistical models can be categorized as probabilistic graphical models, including Naive-Bayes, Hidden Markov Models, Kalman Filters, Ising models and so on. In a probabilistic graphical model, each node represen ...
... probability theory to compactly model complex, real-world phenomena. Many commonly used statistical models can be categorized as probabilistic graphical models, including Naive-Bayes, Hidden Markov Models, Kalman Filters, Ising models and so on. In a probabilistic graphical model, each node represen ...
Technical Note Naive Bayes for Regression
... This paper shows how to apply the naive Bayes methodology to numeric prediction (i.e., regression) tasks by modeling the probability distribution of the target value with kernel density estimators, and compares it to linear regression, locally weighted linear regression, and a method that produces “ ...
... This paper shows how to apply the naive Bayes methodology to numeric prediction (i.e., regression) tasks by modeling the probability distribution of the target value with kernel density estimators, and compares it to linear regression, locally weighted linear regression, and a method that produces “ ...
A Computational Model of Belief - Rochester CS
... way of inference about belief. In these models, a person can believe that roses are red and violets are blue, without believing that roses are red. “Roses are red and violets are blue” is one sentence (denoting one proposition), and “roses are red” is a different sentence (denoting a different propo ...
... way of inference about belief. In these models, a person can believe that roses are red and violets are blue, without believing that roses are red. “Roses are red and violets are blue” is one sentence (denoting one proposition), and “roses are red” is a different sentence (denoting a different propo ...
CS G120 Artificial Intelligence
... Given a set of RV’s X, typically, we are interested in the posterior joint distribution of the query variables Y given specific values e for the evidence variables E Let the hidden variables be H = X - Y – E Then the required calculation of P(Y | E) is done by summing out the hidden variables: P( Y ...
... Given a set of RV’s X, typically, we are interested in the posterior joint distribution of the query variables Y given specific values e for the evidence variables E Let the hidden variables be H = X - Y – E Then the required calculation of P(Y | E) is done by summing out the hidden variables: P( Y ...
Bayesian Reasoning - Bayesian Intelligence
... effortlessly copied from one system to another (to the consternation of those worried about intellectual property rights!), and the labor savings of AI support for bureaucratic applications of rules, medical diagnosis, research assistance, manufacturing control, etc. promises to be enormous. If a se ...
... effortlessly copied from one system to another (to the consternation of those worried about intellectual property rights!), and the labor savings of AI support for bureaucratic applications of rules, medical diagnosis, research assistance, manufacturing control, etc. promises to be enormous. If a se ...
To Developed Tool, an Intelligent Agent for AutomaticKnowledge
... implemented using expert system tools (shells) and intelligent agents or multi-intelligent agents in different domains, we will review a set of this studies which concern with knowledge acquisition approaches and construct expert systems. In [9], The authors describes JavaDON, an open-source expert ...
... implemented using expert system tools (shells) and intelligent agents or multi-intelligent agents in different domains, we will review a set of this studies which concern with knowledge acquisition approaches and construct expert systems. In [9], The authors describes JavaDON, an open-source expert ...
Is there a future for AI without representation?
... In (Brooks 1991b) “traditional” or “central” or “explicit” representations are rejected and the claim is made that: “The best that can be said in our implementation is that one number is passed from a process to another.” (Brooks 1991b, 149). These are said not to be representations, not even implic ...
... In (Brooks 1991b) “traditional” or “central” or “explicit” representations are rejected and the claim is made that: “The best that can be said in our implementation is that one number is passed from a process to another.” (Brooks 1991b, 149). These are said not to be representations, not even implic ...
AI III CS QB - E
... 16. An _________ control structure is also useful if some tasks (nodes) provide negative evidence about the merits of the other tasks (nodes). A. goaling B. operator sub goaling C. Agenda driven D. All of the above Answer Option C 17. The form of the heuristic estimation function for A* is ____ A. f ...
... 16. An _________ control structure is also useful if some tasks (nodes) provide negative evidence about the merits of the other tasks (nodes). A. goaling B. operator sub goaling C. Agenda driven D. All of the above Answer Option C 17. The form of the heuristic estimation function for A* is ____ A. f ...
Document
... • Atomic event: A complete specification of the state of the world about which the agent is uncertain • E.g., if the world consists of only two Boolean variables Cavity and Toothache, then there are 4 distinct atomic events: Cavity = false Toothache = false Cavity = false Toothache = true Cavity ...
... • Atomic event: A complete specification of the state of the world about which the agent is uncertain • E.g., if the world consists of only two Boolean variables Cavity and Toothache, then there are 4 distinct atomic events: Cavity = false Toothache = false Cavity = false Toothache = true Cavity ...
Simple Stochastic Temporal Constraint Networks
... X 1 ,..., X n , and a set of binary stochastic constraints, f ij (δ ) , imposed on the distances ∆ ij between some of these variables. As in the non-stochastic case, such a network can be represented by a directed constraint graph whose nodes correspond to variables and edges represent explicit cons ...
... X 1 ,..., X n , and a set of binary stochastic constraints, f ij (δ ) , imposed on the distances ∆ ij between some of these variables. As in the non-stochastic case, such a network can be represented by a directed constraint graph whose nodes correspond to variables and edges represent explicit cons ...
pdf file
... values of attributes of this object. It is possible to observe the object leading to input information consisting of observable properties. On the basis of these properties information on the values of attributes of the object is derived. This task involves interpretation: interpreting observable pr ...
... values of attributes of this object. It is possible to observe the object leading to input information consisting of observable properties. On the basis of these properties information on the values of attributes of the object is derived. This task involves interpretation: interpreting observable pr ...
Knowledge Representation and Classical Logic
... Both formulas have the same meaning: x has the property P , and there exists an object with the property Q. A closed formula, or a sentence, is a formula without free variables. The universal closure of a formula F is the sentence ∀v1 · · · vn F , where v1 , . . . , vn are the free variables of F . ...
... Both formulas have the same meaning: x has the property P , and there exists an object with the property Q. A closed formula, or a sentence, is a formula without free variables. The universal closure of a formula F is the sentence ∀v1 · · · vn F , where v1 , . . . , vn are the free variables of F . ...
Knowledge Representation and Classical Logic
... Both formulas have the same meaning: x has the property P , and there exists an object with the property Q. A closed formula, or a sentence, is a formula without free variables. The universal closure of a formula F is the sentence ∀v1 · · · vn F , where v1 , . . . , vn are the free variables of F . ...
... Both formulas have the same meaning: x has the property P , and there exists an object with the property Q. A closed formula, or a sentence, is a formula without free variables. The universal closure of a formula F is the sentence ∀v1 · · · vn F , where v1 , . . . , vn are the free variables of F . ...
Solving Large Markov Decision Processes (depth paper)
... or function) schemata and action schemata defined over object classes instead of using explicit states and actions. This representation not only makes it possible to describe large state-space problems, but is also capable of specifying similar decision-making problems (related MDPs) by using one si ...
... or function) schemata and action schemata defined over object classes instead of using explicit states and actions. This representation not only makes it possible to describe large state-space problems, but is also capable of specifying similar decision-making problems (related MDPs) by using one si ...
An Efficient Sampling Scheme For Comparison of Large
... between two graphs. While these approaches are computationally feasible, they are rather naive, as they neglect the structure of the graphs, i.e., their topology. Frequent subgraph mining algorithms, on the other hand, aim to detect subgraphs that are frequent in a given dataset of graphs [Kuramochi ...
... between two graphs. While these approaches are computationally feasible, they are rather naive, as they neglect the structure of the graphs, i.e., their topology. Frequent subgraph mining algorithms, on the other hand, aim to detect subgraphs that are frequent in a given dataset of graphs [Kuramochi ...
Awareness, negation and logical omniscience
... play an important part in the problem of logical omniscience. The existing approaches mainly focus on those three closure properties. Claim 3.1 In General Epistemic Logics, beliefs are closed under implication, valid implication and conjunction. There are some proposals which introduces the notion o ...
... play an important part in the problem of logical omniscience. The existing approaches mainly focus on those three closure properties. Claim 3.1 In General Epistemic Logics, beliefs are closed under implication, valid implication and conjunction. There are some proposals which introduces the notion o ...
Introduction to Artificial Intelligence (Undergraduate Topics in
... in common with formal languages. In this book we will point to such appropriate systems in several places, but not give a systematic introduction. For a first introduction in this field, we refer to Chaps. 22 and 23 in [RN10]. Fuzzy logic, or fuzzy set theory, has developed into a branch of control ...
... in common with formal languages. In this book we will point to such appropriate systems in several places, but not give a systematic introduction. For a first introduction in this field, we refer to Chaps. 22 and 23 in [RN10]. Fuzzy logic, or fuzzy set theory, has developed into a branch of control ...
Constraint Programming - What is behind?
... • a set of variables X={x1,...,xn}, • for each variable xi, a finite set Di of possible values (its domain), and • a set of constraints restricting the values that the variables can simultaneously take. Note that values need not be a set of consecutive integers (although often they are), they need n ...
... • a set of variables X={x1,...,xn}, • for each variable xi, a finite set Di of possible values (its domain), and • a set of constraints restricting the values that the variables can simultaneously take. Note that values need not be a set of consecutive integers (although often they are), they need n ...
A Comparative Analysis of Classification with Unlabelled Data using
... Since the likelihood of no is higher, the learning result of the above instance using Gaussian naïve Bayesian is no. We observe that this result is the same as what we calculated earlier in the example of naïve Bayesian approach. In fact, there also exist some other distributions for naïve Bayesian ...
... Since the likelihood of no is higher, the learning result of the above instance using Gaussian naïve Bayesian is no. We observe that this result is the same as what we calculated earlier in the example of naïve Bayesian approach. In fact, there also exist some other distributions for naïve Bayesian ...
Variational Inference for Dirichlet Process Mixtures
... on the stick-breaking representation of the underlying DP. The algorithm involves two probability distributions—the posterior distribution p and a variational distribution q. The latter is endowed with free variational parameters, and the algorithmic problem is to adjust these parameters so that q a ...
... on the stick-breaking representation of the underlying DP. The algorithm involves two probability distributions—the posterior distribution p and a variational distribution q. The latter is endowed with free variational parameters, and the algorithmic problem is to adjust these parameters so that q a ...