The treasure hunt problem was introduced to describe the problem of planning the path and measurements of a sensor installed on a ground robot, in order to classify multiple targets in an obstacle-populated environment. The use of conventional path-planners like probabilistic roadmaps (PRM) for this purpose requires prior knowledge of the workspace while other online path-planning algorithms rely on the sensor’s ability to form a global map and localize itself in it. However, in unknown workspaces, under environmental pressures like fog, the information captured by the sensor is extremely local leading to a non-convergent global map, which subsequently limits the functioning of the algorithms. Artificial systems implement decision and control policies to optimize a given cost function on one hand, humans use satisficing decision strategies to overcome the limitations of partial information on the other. Satisficing strategies lead to solutions that are not always optimal for a given system, but which are good enough to meet all its needs at a certain level given the constraints on resources. To overcome the limitations of the current artificial systems, this work aims to create the building blocks for an adaptive heuristic path planner which efficiently tackles the treasure hunt problem in unknown workspaces under environmental pressures. Two different path planners that mimic satisficing strategies have been simulated. With the results of this work, adaptive heuristic path planners can further be developed which will improve autonomous area exploration and efficiently solve the treasure hunt problem.