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We examine two strategies for combining the intention and reactive modules for decision making in a real time environment.
A strategy for a player in a Reactive Modules game defines how that player selects enabled guarded commands for execution over successive rounds of the game.
Clearly, dendritic polymers, especially dendrons and dendrimers are expected to fulfill important roles as fundamental, reactive modules for nanoscale polymer syntheses.
Other related formalisms are Reactive Modules (Alur and Henzinger 1999) and Live Sequence Charts (Damm and Harel 2001), which, along with the former, have been applied in the modelling of C. elegans vulval development (Fisher et al. 2005; 2007).
The formal framework we use to study this problem assumes agents are modelled using Reactive Modules, a system modelling language that is used in a range of practical model checking systems.
Reactive modules is a modeling language for reactive systems [ 46].
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During performance, the actions executed by the reactive module are tracked by a high level intention module (also a fusion ART network) that learns to associate sequences of actions with context and goals.
Operating with a sense-act-learn paradigm, the low level reactive module is a fusion ART network that learns action and value policies across the sensory, motor, and feedback channels.
Open image in new window Fig. 2 Pitch angle control module 3) Reactive control module .
However, the equivalent generator is not particularly modeled, because the rotor part and excitation system are separately included in the two-mass shafting module and the reactive control module.
For intention and reinforcement learning to work cooperatively, two strategies are presented for combining the intention module and the reactive learning module for decision making in a real time environment.
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