Formal Definition of a
... The extended Case Factory approach extends the SEASALT architecture with a maintenance mechanism for CBR systems. If a topic agent has access to a CBR system, a CF is provided to maintain the CBR system. To coordinate several CFs a so-called Case Factory Organization (CFO) is provided, which consist ...
... The extended Case Factory approach extends the SEASALT architecture with a maintenance mechanism for CBR systems. If a topic agent has access to a CBR system, a CF is provided to maintain the CBR system. To coordinate several CFs a so-called Case Factory Organization (CFO) is provided, which consist ...
to the neuron`s output. The neuron does not perform other
... In the proposed architecture vectorA has 8-components (ar) and each component is represented by an 8-bit binary number (P 1,0), i.e. by a byte: k= 1 is the most (MSB), k=8 is the least significant bit (LSB). The matrixB dimension is 8x8 and each element b13 is represented by byte too (bs, s =1..8): ...
... In the proposed architecture vectorA has 8-components (ar) and each component is represented by an 8-bit binary number (P 1,0), i.e. by a byte: k= 1 is the most (MSB), k=8 is the least significant bit (LSB). The matrixB dimension is 8x8 and each element b13 is represented by byte too (bs, s =1..8): ...
Parameter Priors for Directed Acyclic Graphical Models
... n-tuple) and xi is a value for Xi. When xi has no incoming arcs in m (no parents), p(xi(paT)stands for p(xi). The local distributions are the n conditional and marginal probability distributions that constitute the factorization of p(x). Each such distribution belongs to the specified family of allo ...
... n-tuple) and xi is a value for Xi. When xi has no incoming arcs in m (no parents), p(xi(paT)stands for p(xi). The local distributions are the n conditional and marginal probability distributions that constitute the factorization of p(x). Each such distribution belongs to the specified family of allo ...
Independence in Relational Languages with Finite Domains
... We may, in rough terms, divide existing languages that claim to be “firstorder (or subsets thereof) probabilistic logics” in two groups. In one group, probability assessments are rather flexible and independence relations are mostly ignored. Nilsson’s and Halpern’s logics are examples, as are severa ...
... We may, in rough terms, divide existing languages that claim to be “firstorder (or subsets thereof) probabilistic logics” in two groups. In one group, probability assessments are rather flexible and independence relations are mostly ignored. Nilsson’s and Halpern’s logics are examples, as are severa ...
Pruning Conformant Plans by Counting Models on Compiled
... retains the uncertainty in the model but assumes full observability, resulting in a heuristic function that is useful in certain problems, but not in those problems where reasoning by cases is not appropriate. For example, if a robot does not know whether it is at distance one or two from the goal, ...
... retains the uncertainty in the model but assumes full observability, resulting in a heuristic function that is useful in certain problems, but not in those problems where reasoning by cases is not appropriate. For example, if a robot does not know whether it is at distance one or two from the goal, ...
SYST 201 Systems Modeling I
... parameters as data accrue – Probabilistic semantics for model interoperability – Efficient exact and approximate computation ©Kathryn Blackmond Laskey ...
... parameters as data accrue – Probabilistic semantics for model interoperability – Efficient exact and approximate computation ©Kathryn Blackmond Laskey ...
Q - Duke Computer Science
... • Random restarts: if your hill-climbing search fails (or returns a result that may not be optimal), restart at a random point in the search space – Not always easy to generate a random state ...
... • Random restarts: if your hill-climbing search fails (or returns a result that may not be optimal), restart at a random point in the search space – Not always easy to generate a random state ...
x - inst.eecs.berkeley.edu
... Lazy learning: keep data around and predict from it at test time 2 Examples ...
... Lazy learning: keep data around and predict from it at test time 2 Examples ...
Advances in the Understanding and Use of Conditional Independence
... methods; this works when the individual factors used to construct the joint density are sufficiently well behaved to allow effective sampling from conditional distributions defined by multiplying together neighboring factors. The same conditions usually also permit computation of maximum likelihood ...
... methods; this works when the individual factors used to construct the joint density are sufficiently well behaved to allow effective sampling from conditional distributions defined by multiplying together neighboring factors. The same conditions usually also permit computation of maximum likelihood ...
CSC 506: Software Engineering and Knowledge Engineering
... More specific problem in AI Solutions = knowledge structures o decision trees o logic and predicate calculus o rules: production systems o description logics, semantic nets, frames o scripts o ontologies ...
... More specific problem in AI Solutions = knowledge structures o decision trees o logic and predicate calculus o rules: production systems o description logics, semantic nets, frames o scripts o ontologies ...
PDF
... In this paper we focus on continuous-time Markov processes having a discrete product state space S = S1 × S2 × · · · × SD , where D is the number of components and the size of each Si is finite. The dynamics of such processes that are also time-homogeneous can be determined by a single rate matrix w ...
... In this paper we focus on continuous-time Markov processes having a discrete product state space S = S1 × S2 × · · · × SD , where D is the number of components and the size of each Si is finite. The dynamics of such processes that are also time-homogeneous can be determined by a single rate matrix w ...
Sparrow2011
... S PARROW is a stochastic local search (SLS) solver for SAT formulae in CNF format [2]. It was named after the mascot of the German city of Ulm. S PARROW 2011 is a variant of S PARROW that was developed for the 2011 SAT competition. The core S PARROW 2011 algorithm is identical to S PARROW; it has be ...
... S PARROW is a stochastic local search (SLS) solver for SAT formulae in CNF format [2]. It was named after the mascot of the German city of Ulm. S PARROW 2011 is a variant of S PARROW that was developed for the 2011 SAT competition. The core S PARROW 2011 algorithm is identical to S PARROW; it has be ...
Decision Sum-Product-Max Networks
... We then perform inference by evaluating the SPMN using a bottom-up pass. In order to integrate the decisions, D, for instance Di , each max node will multiply the value of its children with either 0 or 1 depending on the value of the corresponding decision in the instance. This multiplication is equ ...
... We then perform inference by evaluating the SPMN using a bottom-up pass. In order to integrate the decisions, D, for instance Di , each max node will multiply the value of its children with either 0 or 1 depending on the value of the corresponding decision in the instance. This multiplication is equ ...
Neural Networks and Statistical Models
... used to train perceptrons. Thus, in theory, GLIMs and perceptrons are almost the same thing, but in practice the overlap is not as great as it could be in theory. Polynomial regression can be represented by a diagram of the form shown in Figure 6, in which the arrows from the inputs to the polynomia ...
... used to train perceptrons. Thus, in theory, GLIMs and perceptrons are almost the same thing, but in practice the overlap is not as great as it could be in theory. Polynomial regression can be represented by a diagram of the form shown in Figure 6, in which the arrows from the inputs to the polynomia ...
Solutions of the BCM learning rule in a network of lateral interacting
... The method we use here is a novel direct method in which we study a matrix form of the dynamics using D T , the matrix spanned by the individual input vectors (d). We solve a deterministic matrix equation (e.g., equation (3)) rather than an equation averaged over the inputs. This method enables anal ...
... The method we use here is a novel direct method in which we study a matrix form of the dynamics using D T , the matrix spanned by the individual input vectors (d). We solve a deterministic matrix equation (e.g., equation (3)) rather than an equation averaged over the inputs. This method enables anal ...
Slides - AI-MAS
... semantics, and inference. All propositions/statements are represented as formulae which have a semantics according to the logic in question. Logical system = Formal language + semantics Formal logics gives us a framework to discuss different kinds of reasoning. ...
... semantics, and inference. All propositions/statements are represented as formulae which have a semantics according to the logic in question. Logical system = Formal language + semantics Formal logics gives us a framework to discuss different kinds of reasoning. ...
Using Rewards for Belief State Updates in Partially Observable
... table. We note that in most POMDP examples, the number of possible immediate rewards satisfies this assumption, and is often very small. However, if this assumption is not satisfied, e.g. if reward are continuous, a conditional probability distribution over rewards can still be specified in some par ...
... table. We note that in most POMDP examples, the number of possible immediate rewards satisfies this assumption, and is often very small. However, if this assumption is not satisfied, e.g. if reward are continuous, a conditional probability distribution over rewards can still be specified in some par ...
Advances in Environmental Biology Systems
... number of states is small, such a method may be feasible. With the increase in states of a variable, estimating probabilities directly to all states at one time may inevitably involve biases and inaccuracies. An alternative way is to perform pair-wise comparisons between states for generating their ...
... number of states is small, such a method may be feasible. With the increase in states of a variable, estimating probabilities directly to all states at one time may inevitably involve biases and inaccuracies. An alternative way is to perform pair-wise comparisons between states for generating their ...
Probabilistic State-Dependent Grammars for Plan
... more than their parent nodes. In the example, the driver’s plan originates with the start symbol, N11 =Drive. The driver then chooses among the five possible expansions shown in Figure 1. The production variable, P11 , indicates the production chosen, as well as what symbol on the righthand side is ...
... more than their parent nodes. In the example, the driver’s plan originates with the start symbol, N11 =Drive. The driver then chooses among the five possible expansions shown in Figure 1. The production variable, P11 , indicates the production chosen, as well as what symbol on the righthand side is ...
Learning Basis Functions in Hybrid Domains
... In the context of discrete-state ALP, Patrascu et al. (2002) proposed a greedy approach to learning basis functions. This method is based on the dual ALP formulation and its scores. Although our approach is similar to Patrascu et al. (2002), it is also different in two important ways. First, it is c ...
... In the context of discrete-state ALP, Patrascu et al. (2002) proposed a greedy approach to learning basis functions. This method is based on the dual ALP formulation and its scores. Although our approach is similar to Patrascu et al. (2002), it is also different in two important ways. First, it is c ...
From Certainty Factors to Belief Networks
... In this issue of the journal, Dan and Dudeck provide a critique of the certainty-factor (CF) model, a method for managing uncertainty in rule-based systems. Shortliffe and Buchanan developed the CF model in the mid-1970s for MYCIN, an early expert system for the diagnosis and treatment of meningitis ...
... In this issue of the journal, Dan and Dudeck provide a critique of the certainty-factor (CF) model, a method for managing uncertainty in rule-based systems. Shortliffe and Buchanan developed the CF model in the mid-1970s for MYCIN, an early expert system for the diagnosis and treatment of meningitis ...
Knowledge Representation and Reasoning
... 6.4 Properties of logical systems Important properties of logical systems: Consistency - no theorem of the system contradicts another. Soundness - the system's rules of proof will never allow a false inference from a true premise. If a system is sound and its axioms are true then its theorems a ...
... 6.4 Properties of logical systems Important properties of logical systems: Consistency - no theorem of the system contradicts another. Soundness - the system's rules of proof will never allow a false inference from a true premise. If a system is sound and its axioms are true then its theorems a ...
Metody Inteligencji Obliczeniowej
... extending expert knowledge, adjusting the prior knowledge according to performed tests, etc. Beyond transformations & feature spaces: actively search for info. Intemi software incorporating these ideas coming “soon” ... ...
... extending expert knowledge, adjusting the prior knowledge according to performed tests, etc. Beyond transformations & feature spaces: actively search for info. Intemi software incorporating these ideas coming “soon” ... ...
1 Large Sample Theory 1.1 Basics
... Claim 1.14 (Vector Generalization of Convergence in Probability) Marginal convergence in probability implies joint convergence in probability. Let xn denote a sequence of random vectors and x another random vector. Denote as xn;j the j th p p component of xn . Given that xn;j ! xj as n ! 1 8j = 1; : ...
... Claim 1.14 (Vector Generalization of Convergence in Probability) Marginal convergence in probability implies joint convergence in probability. Let xn denote a sequence of random vectors and x another random vector. Denote as xn;j the j th p p component of xn . Given that xn;j ! xj as n ! 1 8j = 1; : ...