Considerations on Belief Revision in an Action Theory
... transition system framework, by simply combining an account of reasoning about knowledge with an account of belief revision. This in turn could be accomplished by simply specifying a faithful assignment, as in Definition 2, for every set of states. And indeed it is straightforward to incorporate bel ...
... transition system framework, by simply combining an account of reasoning about knowledge with an account of belief revision. This in turn could be accomplished by simply specifying a faithful assignment, as in Definition 2, for every set of states. And indeed it is straightforward to incorporate bel ...
(final)
... Insensitive: explanations cannot be tailored to meet the needs of different users or of different situations. Unresponsive: the system cannot answer follow-up questions or offer an alternative explanation ,if a user does not understand a given explanation. inextensible: new explanation strategies ca ...
... Insensitive: explanations cannot be tailored to meet the needs of different users or of different situations. Unresponsive: the system cannot answer follow-up questions or offer an alternative explanation ,if a user does not understand a given explanation. inextensible: new explanation strategies ca ...
Probabilistic Latent Variable Model for Sparse
... the redundancy present among the elements of the input stream. Consider the context of basis decomposition techniques. The data vector v (or the underlying generative distribution in the case of a latent variable model) is approximated as Wh where the columns of W are basis vectors and elements hi o ...
... the redundancy present among the elements of the input stream. Consider the context of basis decomposition techniques. The data vector v (or the underlying generative distribution in the case of a latent variable model) is approximated as Wh where the columns of W are basis vectors and elements hi o ...
Learning Distinctions and Rules in a Continuous World through
... proposes looking for new distinctions when the probability associated with a prospective contingency on two events does not match the probability associated with the retrospective contingency on those same events. He also uses this mismatch in probabilities to indicate that a contingency may only ho ...
... proposes looking for new distinctions when the probability associated with a prospective contingency on two events does not match the probability associated with the retrospective contingency on those same events. He also uses this mismatch in probabilities to indicate that a contingency may only ho ...
The Independent Choice Logic and Beyond
... to give the consequences of the choices. The independent choice logic extends probabilistic Horn abduction in allowing for multiple agents each making their own choices ...
... to give the consequences of the choices. The independent choice logic extends probabilistic Horn abduction in allowing for multiple agents each making their own choices ...
A MANUSCRIPT OF KNOWLEDGE REPRESENTATION
... networks are closely related to another form of knowledge representation called frame systems. In fact, frame systems and semantic networks can be identical in their expressiveness but use different representation metaphors. While the semantic network metaphor is that of a graph with concept nodes l ...
... networks are closely related to another form of knowledge representation called frame systems. In fact, frame systems and semantic networks can be identical in their expressiveness but use different representation metaphors. While the semantic network metaphor is that of a graph with concept nodes l ...
Guided Incremental Construction of Belief Networks
... There are several ways to do this. Laskey and Mahoney [10] define several influence combination methods to combine conditional probability distributions, one of the principal types being parametric causal models like noisy-or. The noisy-or model [14, 17] allows one to compactly specify a conditional ...
... There are several ways to do this. Laskey and Mahoney [10] define several influence combination methods to combine conditional probability distributions, one of the principal types being parametric causal models like noisy-or. The noisy-or model [14, 17] allows one to compactly specify a conditional ...
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... that they can try to establish themselves. Specialists communicate with each other by passing messages, and each specialist has local procedures specifying how it responds to each kind of message. Uncertainty calculations in CSRL are used to de termine when a hypothesis has been established. The ke ...
... that they can try to establish themselves. Specialists communicate with each other by passing messages, and each specialist has local procedures specifying how it responds to each kind of message. Uncertainty calculations in CSRL are used to de termine when a hypothesis has been established. The ke ...
Dynamic Programming for Partially Observable Stochastic Games
... can be converted to a normal form representation with hidden state, by a recursive construction. Given the sets of strategies and the value (or payoff) functions for a horizon t game, the sets of strategies and value functions for the horizon t game are constructed by exhaustive backup, as in the ca ...
... can be converted to a normal form representation with hidden state, by a recursive construction. Given the sets of strategies and the value (or payoff) functions for a horizon t game, the sets of strategies and value functions for the horizon t game are constructed by exhaustive backup, as in the ca ...
PDF
... algorithms systematically expore a search tree of possible solutions [12]. They construct a consistent partial solution by extending it one variable assignment at a time and backtracking on failure, until every variable is assigned a consistent value under the problem constraints. Unfortunately, bad ...
... algorithms systematically expore a search tree of possible solutions [12]. They construct a consistent partial solution by extending it one variable assignment at a time and backtracking on failure, until every variable is assigned a consistent value under the problem constraints. Unfortunately, bad ...
Probabilistic graphical models in artificial intelligence
... The coexistence of alternative models gave rise to a sour debate about the appropriateness of the different theories. The positions were hard and in some cases excluded the possibility of alternative positions. This strong view was more common in the field of probability theory, perhaps under the in ...
... The coexistence of alternative models gave rise to a sour debate about the appropriateness of the different theories. The positions were hard and in some cases excluded the possibility of alternative positions. This strong view was more common in the field of probability theory, perhaps under the in ...
Perspectives on the Theory and Practice of Belief Functions
... knowledge of frequencies in the ideal picture. This analogy is strongest when extensive frequency data is available for our problem (in this case, it is customary to talk about “empirical Bayes”). When our evidence does not consist of frequencies, the analogy may or may not be convincing. ...
... knowledge of frequencies in the ideal picture. This analogy is strongest when extensive frequency data is available for our problem (in this case, it is customary to talk about “empirical Bayes”). When our evidence does not consist of frequencies, the analogy may or may not be convincing. ...
Sets of Boolean Connectives that make Argumentation Easier
... polynomial-time Turing machines with an NP oracle. A complete problem for DP is Critical-Sat, the problem to decide whether a given formula in 3CNF is unsatisfiable but removing any of its clauses makes it satisfiable [PW88]. For our hardness results we employ logspace many-one reductions, defined a ...
... polynomial-time Turing machines with an NP oracle. A complete problem for DP is Critical-Sat, the problem to decide whether a given formula in 3CNF is unsatisfiable but removing any of its clauses makes it satisfiable [PW88]. For our hardness results we employ logspace many-one reductions, defined a ...
A theoretical study of Y structures for causal discovery
... Furthermore, it is known that members of an independence (Markov) equivalence class of causal Bayesian network (CBN) models are indistinguishable using only probabilistic dependence and independence relationships among the observed variables. There are several algorithms for reliably identifying (so ...
... Furthermore, it is known that members of an independence (Markov) equivalence class of causal Bayesian network (CBN) models are indistinguishable using only probabilistic dependence and independence relationships among the observed variables. There are several algorithms for reliably identifying (so ...
Fixed-parameter complexity in AI and nonmonotonic reasoning
... basic and most fundamental structural property considered in the context of CSPs (and conjunctive database queries) is acyclicity. It was recognized independently in AI and in database theory that acyclic CSPs (respectively, conjunctive queries) are solvable in polynomial time. There are many equiva ...
... basic and most fundamental structural property considered in the context of CSPs (and conjunctive database queries) is acyclicity. It was recognized independently in AI and in database theory that acyclic CSPs (respectively, conjunctive queries) are solvable in polynomial time. There are many equiva ...
Automating Operational Business Decisions Using Artificial
... Making business decisions nowadays is increasingly reliant upon analyzing very large data-sets and the complex relations between them. This makes the task time consuming and complex for humans to carry out accurately. Algorithms could support or even take over this task by learning to make certain d ...
... Making business decisions nowadays is increasingly reliant upon analyzing very large data-sets and the complex relations between them. This makes the task time consuming and complex for humans to carry out accurately. Algorithms could support or even take over this task by learning to make certain d ...
An efficient approach for finding the MPE in belief networks
... tions for the MPE is simply given right instantiation of all variables. This means that finding the MPE can be a search problem. We can use search with back tracking techniques to find the MPE, but it may not be an efficient way because the search complexity is exponential with respect to the number ...
... tions for the MPE is simply given right instantiation of all variables. This means that finding the MPE can be a search problem. We can use search with back tracking techniques to find the MPE, but it may not be an efficient way because the search complexity is exponential with respect to the number ...
KNOWLEDGE, REPRESENTATION, AND RATIONAL SELF
... which implicit beliefs follow from given explicit beliefs and given quantities of resources, for example, in terms of how many applications of Modus Ponens are needed to derive a conclusion. While theories of knowledge that take resources into account are a step in the right direction, they have sev ...
... which implicit beliefs follow from given explicit beliefs and given quantities of resources, for example, in terms of how many applications of Modus Ponens are needed to derive a conclusion. While theories of knowledge that take resources into account are a step in the right direction, they have sev ...
ADVANCES IN KNOWLEDGE ACQUISITION AND
... dynamic process of collaboration and competition among a community of active agents. Such an approach to knowledge acquisition can identify emerging behaviors such as hubs and authorities12 and communities8. In the case of Kobti et al.’s article in this issue, emergent behavior can identify speciali ...
... dynamic process of collaboration and competition among a community of active agents. Such an approach to knowledge acquisition can identify emerging behaviors such as hubs and authorities12 and communities8. In the case of Kobti et al.’s article in this issue, emergent behavior can identify speciali ...
Infinite-Horizon Proactive Dynamic DCOPs
... with changes between them. There are a number of algorithms that handle various changes such as addition/removal of agents [26] or changes in the topology of the coordination graph [35]. In this paper, we focus on the problem where only the reward functions can change. This is a popular Dynamic DCOP ...
... with changes between them. There are a number of algorithms that handle various changes such as addition/removal of agents [26] or changes in the topology of the coordination graph [35]. In this paper, we focus on the problem where only the reward functions can change. This is a popular Dynamic DCOP ...
Distributed multi-agent probabilistic reasoning with Bayesian networks
... A piece of evidence is obtained by one agent’s observation, made at one time, of a set of variables contained in its subnet. Different agents may acquire pieces of evidence from their local sources asynchronously in parallel. Pearl [10] classifies evidence into specific and virtual. Specific evidenc ...
... A piece of evidence is obtained by one agent’s observation, made at one time, of a set of variables contained in its subnet. Different agents may acquire pieces of evidence from their local sources asynchronously in parallel. Pearl [10] classifies evidence into specific and virtual. Specific evidenc ...
Knowledge Representation in Artificial Intelligence using
... concepts to be acquired and previously acquired concepts. Knowledge is about information that can be used or applied, that is, it is information that has been contextualized in a certain domain, and therefore, any piece of knowledge is related with more knowledge in a particular and different way in ...
... concepts to be acquired and previously acquired concepts. Knowledge is about information that can be used or applied, that is, it is information that has been contextualized in a certain domain, and therefore, any piece of knowledge is related with more knowledge in a particular and different way in ...
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... feedback—see Section 2 for details). They reach the following conclusion [Pearl 2000, p. 242].1 In sum, for recursive models, the causal model framework does not add any restrictions to counterfactuals beyond those imposed by Lewis’s framework; the very general concept of closest worlds is sufficien ...
... feedback—see Section 2 for details). They reach the following conclusion [Pearl 2000, p. 242].1 In sum, for recursive models, the causal model framework does not add any restrictions to counterfactuals beyond those imposed by Lewis’s framework; the very general concept of closest worlds is sufficien ...
Linear Logic 15-819K: Logic Programming Lecture 12 Frank Pfenning
... Hidden in the judgment are other assumptions, usually abbreviated as Γ, which can be used arbitrarily often (including not at all), and are therefore called the unrestricted assumptions. If we need to make them explicit in a rule we will write Γ; ∆ `` C true where ∆ abbreviates the resources. As in ...
... Hidden in the judgment are other assumptions, usually abbreviated as Γ, which can be used arbitrarily often (including not at all), and are therefore called the unrestricted assumptions. If we need to make them explicit in a rule we will write Γ; ∆ `` C true where ∆ abbreviates the resources. As in ...
7. Propositional Logic Rational Thinking, Logic, Resolution
... We need a time index for each proposition to represent the validity of the proposition over time → further expansion of the rules. → More powerful logics exist, in which we can use object variables. → First-Order Predicate Logic ...
... We need a time index for each proposition to represent the validity of the proposition over time → further expansion of the rules. → More powerful logics exist, in which we can use object variables. → First-Order Predicate Logic ...