Execution Monitoring with Quantitative Temporal Bayesian Networks

AAAI Conferences

The goal of execution monitoring is to determine whether a system or person is following a plan appropriately. Monitoring information may be uncertain, and the plan being monitored may have complex temporal constraints. We develop a new framework for reasoning under uncertainty with quantitative temporal constraints - Quantitative Temporal Bayesian Networks - and we discuss its application to plan-execution monitoring. QTBNs extend the major previous approaches to temporal reasoning under uncertainty: Time Nets (Kanazawa 1991), Dynamic Bayesian Networks and Dynamic Object Oriented Bayesian Networks (Friedman, Koller, & Pfeffer 1998). We argue that Time Nets can model quantitative temporal relationships but cannot easily model the changing values of fluents, while DBNs and DOOBNs naturally model fluents, but not quantitative temporal relationships. Both capabilities are required for execution monitoring, and are supported by QTBNs.


Max-norm Projections for Factored MDPs

AAAI Conferences

In the MDP framework, the system is modeled via a set of states which evolve stochastically. The key problem with this representation is that, in virtually any real-life domain, the state space is quite large. However, many large MDPs have significant internal structure, and can be modeled compactly if the structure is exploited in the representation. Factored MDPs [Boutilier et al. 1999] are one approach to representing large structured MDPs compactly. In this framework, a state is implicitly described by an assignment to some set of state variables. A dynamic Bayesian network (DBN)[Dean and Kanazawa 1989] can then allow a compact representation of the transition model, by exploiting the fact that the transition of a variable often depends only on a small number of other variables.


Brendan Burns, Clayton T. Morrison, Paul Cohen

AAAI Conferences

A current popular approach to representing time in Bayesian belief networks is through Dynamic Bayesian Networks (DBNs) (Dean & Kanazawa 1989). DBNs connect sequences of entire Bayes networks, each representing a situation at a snapshot in time. We present an alternative method for incorporating time into Bayesian belief networks that utilizes abstractions of temporal representation. This method maintains the principled Bayesian approach to reasoning under uncertainly, providing explicit representation of sequence and potentially complex temporal relationships, while also decreasing overall network complexity compared to DBNs.


Quantitative Temporal Relationships in Dynamic Bayesian Models

AAAI Conferences

The goal of execution monitoring is to determine whether a system or person is fonowmg a plan appropriately. Monitoring information may be uncertain, and the plan being monitored may have complex temporal constraints. We develop a new framework for reasoning under uncertainty with quantitative temporal constraints - Quantitative Temporal Dynamic Bayesian Networks - and we discuss its application to plan-execution monitoring. QTDBNs extend the major previous approaches to temporal reasoning under uncertainty: Time Nets (Kanazawa 1991), Dynamic Bayesian Networks and Dynamic Object Oriented Bayesian Networks (Friedman, Koller, £" Pfeffer 1998). We argue that Time Nets can model quantitative temporal relationships but cannot easily model the changing values of fluents, while DBNs and DOOBNs naturally model fluents, but not quantitative temporal relationships. Both capabilities are required for execution monitoring, and are supported by QTDBNs. Introduction The goal of execution monitoring is to determine whether a system or person is following a plan appropriately or is heading toward a failure state. Most execution monitoring systems build an internal model of the domain and use sensor inputs to update the model. Because sensors are error-prone, the execution monitoring process must be able to reason under uncertainty. Bayesian belief networks that support plan execution monitoring under uncertainty can be generated directly from the plans being monitored (Huber, Durfee, & Wellman 1994).


Application of Variational Bayesian Approach to Speech Recognition

Neural Information Processing Systems

Application of V ariational Bayesian Approach to Speech Recognition Shinji Watanabe, Y asuhiro Minami, Atsushi Nakamura and Naonori Ueda NTT Communication Science Laboratories, NTT Corporation 2-4, Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan {watanabe,minami,ats,ueda}@cslab.kecl.ntt.co.jp Abstract In this paper, we propose a Bayesian framework, which constructs shared-state triphone HMMs based on a variational Bayesian approach, and recognizes speech based on the Bayesian prediction classification; variational Bayesian estimation and clustering for speech recognition (VBEC). An appropriate model structure with high recognition performance can be found within a VBEC framework. Unlike conventional methods, including BIC or MDL criterion based on the maximum likelihood approach, the proposed model selection is valid in principle, even when there are insufficient amounts of data, because it does not use an asymptotic assumption. In acoustic modeling, a triphone-based hidden Markov model (triphone HMM) has been widely employed. The triphone is a context dependent phoneme unit that considers both the preceding and following phonemes.