In this paper, we consider the problem of filtering in relational hidden Markov models. We present a compact representation for such models and an associated logical particle filtering algorithm. Each particle contains a logical formula that describes a set of states. The algorithm updates the formulae as new observations are received. Since a single particle tracks many states, this filter can be more accurate than a traditional particle filter in high dimensional state spaces, as we demonstrate in experiments
Paper WA2―5International audienceWe consider particle filters in a model where the hidden states and...
Filtering denotes any method whereby an agent updates its belief state—its knowledge of the state of...
Exact optimal state estimation for discrete-time Boolean dy-namical systems may become impractical c...
In this paper, we consider the problem of filtering in relational hidden Markov models. We present a...
Filtering denotes any method whereby an agent updates its belief state - its knowledge of the state ...
One of the current challenges in artificial intelligence is modeling dynamic environments that chang...
We introduce a probabilistic language and a fast inference algorithm for state estimation in hybrid ...
Abstract Logical Filtering is the problem of tracking the possible states of a world (belief state) ...
AbstractLogical filtering is the process of updating a belief state (set of possible world states) a...
AbstractThis paper derives a particle filter algorithm within the Dempster–Shafer framework. Particl...
Recall that the main difficulty with particle filtering is that with a high dimensional state variab...
We consider the on-line Bayesian analysis of data by using a hidden Markov model, where inference is...
We consider the numerical approximation of the filtering problem in high dimensions, that is, when t...
We propose a probabilistic logic programming framework for the state estimation problem in dynamic r...
The state space model has been widely used in various fields including economics, finance, bioinform...
Paper WA2―5International audienceWe consider particle filters in a model where the hidden states and...
Filtering denotes any method whereby an agent updates its belief state—its knowledge of the state of...
Exact optimal state estimation for discrete-time Boolean dy-namical systems may become impractical c...
In this paper, we consider the problem of filtering in relational hidden Markov models. We present a...
Filtering denotes any method whereby an agent updates its belief state - its knowledge of the state ...
One of the current challenges in artificial intelligence is modeling dynamic environments that chang...
We introduce a probabilistic language and a fast inference algorithm for state estimation in hybrid ...
Abstract Logical Filtering is the problem of tracking the possible states of a world (belief state) ...
AbstractLogical filtering is the process of updating a belief state (set of possible world states) a...
AbstractThis paper derives a particle filter algorithm within the Dempster–Shafer framework. Particl...
Recall that the main difficulty with particle filtering is that with a high dimensional state variab...
We consider the on-line Bayesian analysis of data by using a hidden Markov model, where inference is...
We consider the numerical approximation of the filtering problem in high dimensions, that is, when t...
We propose a probabilistic logic programming framework for the state estimation problem in dynamic r...
The state space model has been widely used in various fields including economics, finance, bioinform...
Paper WA2―5International audienceWe consider particle filters in a model where the hidden states and...
Filtering denotes any method whereby an agent updates its belief state—its knowledge of the state of...
Exact optimal state estimation for discrete-time Boolean dy-namical systems may become impractical c...