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The availability bias or heuristic states that the probability of recent and salient or rare but vivid events is overestimated and the probability of remote, less memorable or common, ordinary events is underestimated.
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The proposed approach can easily be adapted to other modern heuristic state-space planners.
The track filter is based on a two-stage paradigm of abnormal sensors detection and heuristic state estimation fusion.
This paper enhances the GRT planner, an efficient domain-independent heuristic state-space planner, with the ability to consider multiple criteria.
Most recent strides in scaling up planning have centered around two competing themes disjunctive planners, exemplified by Graphplan, and heuristic state search planners, exemplified by UNPOP, HSP and HSP-r.
Secondly, the gating strategy enables the development of a heuristic state estimation algorithm when sequential Monte Carlo (SMC) implementation of the PHD filter is investigated, where the measurements are used to drive the particle clustering within the space gate.
For instance, one of the proposed heuristics states that each software requirement must be related to its main concern.
Other cognitive biases may occur because of the "availability heuristic", which states that reasoning tends to be based on immediately available information.
In this paper, we present a heuristic for state reduction of ISFSMs.
The proposed heuristic is stated in detail as follows: Step 1 (Lines 1 2): Initialization.
GA [46] was a heuristic, which stated that it estimated a solution and generated the optimized results.
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