A model for reasoning about persistence and causation
Computational Intelligence
Coloured Petri nets: basic concepts, analysis methods and practical use, volume 3
Coloured Petri nets: basic concepts, analysis methods and practical use, volume 3
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Dynamic probabilistic relational models
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IBAL: a probabilistic rational programming language
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Causal independence for probability assessment and inference using Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Continuous refinement of agent resource estimates
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Suspending and resuming tasks in BDI agents
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Generating plans in concurrent, probabilistic, over-subscribed domains
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
CTPPL: a continuous time probabilistic programming language
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Business process mining from e-commerce web logs
BPM'13 Proceedings of the 11th international conference on Business Process Management
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Agents that handle complex processes evolving over a period of time need to be able to monitor the state of the process. Since the evolution of a process is often stochastic, this requires probabilistic monitoring of processes. A probabilistic process modeling language is needed that can adequately capture our uncertainty about the process execution. We present a language for describing probabilistic process models. This language is functional in nature, and the paper argues that a functional language provides a natural way to specify process models. In our framework, processes have both states and values. Processes may execute sequentially or in parallel, and we describe two alternative forms of parallelism. An inference algorithm is presented that constructs a dynamic Bayesian network, containing a variable for every subprocess that is executed during the course of executing a process. We present a detailed example demonstrating the naturalness of the language.