Compositional Belief Update
... without giving any a priori preference to one or the other of the belief sets, but aiming to achieve a balanced resolution of conflicts. Such a merging might be used to combine the belief states of different agents, so as to come up with a joint course of action based on some sort of “all things consi ...
... without giving any a priori preference to one or the other of the belief sets, but aiming to achieve a balanced resolution of conflicts. Such a merging might be used to combine the belief states of different agents, so as to come up with a joint course of action based on some sort of “all things consi ...
full paper - Frontiers in Artificial Intelligence and Applications (FAIA)
... KBs can be represented exactly by a formula with good properties ; in this case, the formula can give the answer to any query. To summarize, approximations can help saving a lot of time when answering queries (for instance in an on-line framework), especially if they can be reasoned with efficiently ...
... KBs can be represented exactly by a formula with good properties ; in this case, the formula can give the answer to any query. To summarize, approximations can help saving a lot of time when answering queries (for instance in an on-line framework), especially if they can be reasoned with efficiently ...
POMDP solution methods - Department of Computer Science
... Partially observable Markov decision processes (POMDPs) provide a natural model for sequential decision making under uncertainty. This model augments a well-researched framework of Markov decision processes (MDPs) [Howard, 1960, Puterman, 1994] to situations where an agent cannot reliably identify t ...
... Partially observable Markov decision processes (POMDPs) provide a natural model for sequential decision making under uncertainty. This model augments a well-researched framework of Markov decision processes (MDPs) [Howard, 1960, Puterman, 1994] to situations where an agent cannot reliably identify t ...
CS325 Artificial Intelligence Chs. 9, 12 – Knowledge Representation
... O’Reilly RC, Busby RS, Soto R (2003). Three forms of binding and their neural substrates: Alternatives to temporal synchrony. In Cleeremans A, ed., The Unity of Consciousness: Binding, Integration and Dissociation. Oxford University Press, Oxford Quiroga R, Reddy L, Kreiman G, et al. (2005). Invaria ...
... O’Reilly RC, Busby RS, Soto R (2003). Three forms of binding and their neural substrates: Alternatives to temporal synchrony. In Cleeremans A, ed., The Unity of Consciousness: Binding, Integration and Dissociation. Oxford University Press, Oxford Quiroga R, Reddy L, Kreiman G, et al. (2005). Invaria ...
Modular Basic Action Theories - Department of Computer Science
... The situation calculus (SC) is a well known and popular logical theory for reasoning about events and actions. There are several different formulations of SC. According to John McCarthy the history is the following: “(McCarthy 1959) proposed mathematical logic as a tool for representing facts about ...
... The situation calculus (SC) is a well known and popular logical theory for reasoning about events and actions. There are several different formulations of SC. According to John McCarthy the history is the following: “(McCarthy 1959) proposed mathematical logic as a tool for representing facts about ...
probabilistic methods for location estimation in wireless
... given set of calibration points and observations see (Castro et al., 2001; Roos et al., 2002a; Schwaighofer et al., 2003; Youssef et al., 2003). When we then want to position a device with current signal readings o, we calculate the probabilities p(l | o) for each possible location l using the Bayes ...
... given set of calibration points and observations see (Castro et al., 2001; Roos et al., 2002a; Schwaighofer et al., 2003; Youssef et al., 2003). When we then want to position a device with current signal readings o, we calculate the probabilities p(l | o) for each possible location l using the Bayes ...
Negation Without Negation in Probabilistic Logic Programming
... “noise” variables N1 , N2 , . . . , NN . Each noise variable appears exactly once as a rule head, in special probabilistic rules called probabilistic facts with the form pi : ni . Other probabilistic rules are actually only syntactic sugar: p : head ← body is short for p : ni and head ← ni ∧ body, w ...
... “noise” variables N1 , N2 , . . . , NN . Each noise variable appears exactly once as a rule head, in special probabilistic rules called probabilistic facts with the form pi : ni . Other probabilistic rules are actually only syntactic sugar: p : head ← body is short for p : ni and head ← ni ∧ body, w ...
MAI0203 Lecture 7: Inference and Predicate Calculus
... interpretation of und holds that is valid iff is ...
... interpretation of und holds that is valid iff is ...
ECAI Paper PDF - MIT Computer Science and Artificial Intelligence
... variables, such that a set of constraints is satisfied. Formalisms for soft constraints aim at more closely integrating constraint satisfaction and optimization. Soft constraints extend hard constraints by defining preference levels for the constraints, such that assignments are associated with an e ...
... variables, such that a set of constraints is satisfied. Formalisms for soft constraints aim at more closely integrating constraint satisfaction and optimization. Soft constraints extend hard constraints by defining preference levels for the constraints, such that assignments are associated with an e ...
Benchmarks Description
... When the value of modularity Q used to generate the instances is very high, there exist some instances having a very small refutation. This happens because most of the clauses relate variables of the same community, and hence it is more likely to find a small unsatisfiable set of clauses which only ...
... When the value of modularity Q used to generate the instances is very high, there exist some instances having a very small refutation. This happens because most of the clauses relate variables of the same community, and hence it is more likely to find a small unsatisfiable set of clauses which only ...
Distributed Systems Diagnosis Using Belief
... correct on polytrees and can be used as an approximation on general networks. Belief propagation passes probabilistic messages between the nodes and can be iterated until convergence (guaranteed only for polytrees); otherwise, it can be stopped at certain number of iterations. The algorithm computes ...
... correct on polytrees and can be used as an approximation on general networks. Belief propagation passes probabilistic messages between the nodes and can be iterated until convergence (guaranteed only for polytrees); otherwise, it can be stopped at certain number of iterations. The algorithm computes ...
Conformant Planning Heuristics Based on Plan Reuse in Belief States
... were proposed. An additive heuristic that obtains the heuristic value of a given belief state by summing the heuristic value of each world state is sensitive to the progress of the search procedure and often guides a search algorithm efficiently. However, it suffers from the problem of overestimatin ...
... were proposed. An additive heuristic that obtains the heuristic value of a given belief state by summing the heuristic value of each world state is sensitive to the progress of the search procedure and often guides a search algorithm efficiently. However, it suffers from the problem of overestimatin ...
Using Artificial Neural Network to Predict Collisions on Horizontal
... roadway route was subdivided into road sections with individual horizontal tangents, in which the vertical curves were determined and thus the combination types of horizontal tangents and vertical curves. This method allows exploring the interaction of combined horizontal tangent and vertical alignm ...
... roadway route was subdivided into road sections with individual horizontal tangents, in which the vertical curves were determined and thus the combination types of horizontal tangents and vertical curves. This method allows exploring the interaction of combined horizontal tangent and vertical alignm ...
AAAI Proceedings Template
... virtues which include its impressive speed in performing deductions in large knowledge bases which may have long inference chains. As we discuss below, XSB Prolog is, however, a rather basic language for knowledge representation compared to languages such as KIF [Genesereth, 1994] or CycL [Cycorp, 2 ...
... virtues which include its impressive speed in performing deductions in large knowledge bases which may have long inference chains. As we discuss below, XSB Prolog is, however, a rather basic language for knowledge representation compared to languages such as KIF [Genesereth, 1994] or CycL [Cycorp, 2 ...
Exploiting Anonymity and Homogeneity in Factored
... coming into s0 . Note that αt (s) = 0 for all t > 0 and s and α0 is the starting belief distribution over states. The agent policy is obtained by normalizing {xt (s, a)}, i.e., : ...
... coming into s0 . Note that αt (s) = 0 for all t > 0 and s and α0 is the starting belief distribution over states. The agent policy is obtained by normalizing {xt (s, a)}, i.e., : ...
Lecture 05 Part A - First Order Logic (FOL)
... Nono, an enemy of America, has some missiles, and all of its missiles were sold to it by Colonel West, who is American. ... it is a crime for an American to sell weapons to hostile nations: ...
... Nono, an enemy of America, has some missiles, and all of its missiles were sold to it by Colonel West, who is American. ... it is a crime for an American to sell weapons to hostile nations: ...
Exploiting Anonymity and Homogeneity in Factored Dec
... Also, given a set of constants, {dsi ,ai }i∈P , the joint reward P for all agents at a time step is expressed as the sum of individual rewards: i∈P R(si , ai , dsi ,ai ). Note that this is not equivalent to complete reward independence, as there is dependence on numbers of other agents. • ϕ is the ...
... Also, given a set of constants, {dsi ,ai }i∈P , the joint reward P for all agents at a time step is expressed as the sum of individual rewards: i∈P R(si , ai , dsi ,ai ). Note that this is not equivalent to complete reward independence, as there is dependence on numbers of other agents. • ϕ is the ...
The Rules of Logic Composition for the Bayesian - IME-USP
... more probable than anywhere in H ; If θ ∈ T we want to accept H , for θ is less probable than somewhere in H . The minimization of this loss function gives the optimal test: Accept H iff ev(H ) ≥ ϕ = (b +c)/(a +c) . Note that this loss function is dependent on the observed sample (via the likelihood ...
... more probable than anywhere in H ; If θ ∈ T we want to accept H , for θ is less probable than somewhere in H . The minimization of this loss function gives the optimal test: Accept H iff ev(H ) ≥ ϕ = (b +c)/(a +c) . Note that this loss function is dependent on the observed sample (via the likelihood ...
Belief Revision in Multi-Agent Systems
... can be obtained through a scheduling algorithm [de Kleer, 1986b] and an agenda which contains every node with a non empty label and pending consumers. The problem solver repeatedly chooses one of these consumers, executes it, and then removes it, until there are no nodes left on the agenda. The main ...
... can be obtained through a scheduling algorithm [de Kleer, 1986b] and an agenda which contains every node with a non empty label and pending consumers. The problem solver repeatedly chooses one of these consumers, executes it, and then removes it, until there are no nodes left on the agenda. The main ...
Artificial Intelligence and Expert Systems
... Explanation Subsystem (Justifier) Knowledge Refining System ...
... Explanation Subsystem (Justifier) Knowledge Refining System ...
Artificial Intelligence and Expert Systems
... Explanation Subsystem (Justifier) Knowledge Refining System ...
... Explanation Subsystem (Justifier) Knowledge Refining System ...
Aalborg Universitet Learning dynamic Bayesian networks with mixed variables Bøttcher, Susanne Gammelgaard
... In a Bayesian network, the set of random variables X is fixed. To model a multivariate time series we need a framework, where we allow the set of random variables to vary with time. For this we use dynamic Bayesian networks, defined as below. This definition is consistent with the exposition in Murp ...
... In a Bayesian network, the set of random variables X is fixed. To model a multivariate time series we need a framework, where we allow the set of random variables to vary with time. For this we use dynamic Bayesian networks, defined as below. This definition is consistent with the exposition in Murp ...
Explaining Bayesian Networks using Argumentation
... for common sense reasoning tasks. This justifies a scientific interest in models of argumentation that incorporate probabilities. In this paper we formalise a new method to extract arguments from a BN, in which we first extract an intermediate support structure that guides the argument construction ...
... for common sense reasoning tasks. This justifies a scientific interest in models of argumentation that incorporate probabilities. In this paper we formalise a new method to extract arguments from a BN, in which we first extract an intermediate support structure that guides the argument construction ...
The Intelligence of Dual Simplex Method to Solve Linear Fractional
... normally quite clear cut, well described, and crisp. They can generally be modelled and solved by using classical mathematics which is dichotomous in character. If vagueness enters, it is normally of the stochastic kind which can properly be modelled by using probability theory. This is true for man ...
... normally quite clear cut, well described, and crisp. They can generally be modelled and solved by using classical mathematics which is dichotomous in character. If vagueness enters, it is normally of the stochastic kind which can properly be modelled by using probability theory. This is true for man ...
(PPT, 202KB)
... complex algebraic representations. It is an example of how finding the right formalism can enable new solutions. ...
... complex algebraic representations. It is an example of how finding the right formalism can enable new solutions. ...