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Concurrent constraint programming Sar89,SR90] is a simple and powerful model of concurrent computation based on the notions of store-as-constraint and process as information transducer. The store-as-valuation conception of von Neumann computing is replaced by the notion that the store is a constraint (a nite representation of a possibly innnite set of(More)
In this paper we introduce a new class of labelled transition systems-Labelled Markov Processes and deene bisimulation for them. Labelled Markov processes are probabilistic labelled transition systems where the state space is not necessarily discrete. We assume that the state space is a certain type of common metric space called an analytic space. We show(More)
Measurement-based quantum computation has emerged from the physics community as a new approach to quantum computation where the notion of measurement is the main driving force of computation. This is in contrast with the more traditional circuit model that is based on unitary operations. Among measurement-based quantum computation methods, the recently(More)
We introduce a new notion of bisimulation, called event bisimulation on labelled Markov processes (LMPs) and compare it with the, now standard, notion of probabilistic bisimulation, originally due to Larsen and Skou. Event bisimulation uses a sub σ-algebra as the basic carrier of information rather than an equivalence relation. The resulting notion is thus(More)
The notion of process equivalence of probabilistic processes is sensitive to the exact probabilities of transitions. Thus, a slight change in the transition probabilities will result in two equivalent processes being deemed no longer equivalent. This instability is due to the quantitative nature of probabilistic processes. In a situation where the process(More)
We observe that equivalence is not a robust concept in the presence of numerical information-such as probabilities-in the model. We develop a metric analogue of weak bisimulation in the spirit of our earlier work on metric analogues for strong bisimulation. We give a fixed point characterization of the metric. This makes available coinductive reasoning(More)
Labelled Markov processes are probabilistic versions of labelled transition systems. Labelled transition systems where the final state is.. Labelled Markov processes are probabilistic versions of labelled transition systems. Labelled transition systems where the final state is.. Systems that can be in a state and have transitions between states; these(More)
Markov decision processes (MDPs) offer a popular mathematical tool for planning and learning in the presence of uncertainty (Boutilier, Dean, & Hanks 1999). MDPs are a standard formalism for describing multi-stage decision making in probabilistic environments. The objective of the decision making is to maximize a cumulative measure of long-term performance,(More)
We study approximate reasoning about continuous-state labeled Markov processes. We show how to approximate a labeled Markov process by a family of finite-state labeled Markov chains. We show that the collection of labeled Markov processes carries a Polish space structure with a countable basis given by finite state Markov chains with rational probabilities.(More)