Technocratic Development Planning Policies

Technocratic Values and Uneven Development in the “Smart City"

Reinforcement Planning: Planners as Policies

Introduction. State-of-the-art robotic systems [1, 2, 3] increasingly rely on search-based planning or optimal control methods to guide decision making. Similar observations can be made about computer game engines. Such methods are nearly always extremely crude approximations to the reality encountered by the robot: they consider a simplified model of the robot (as a point, or a “flying brick”)...

Iran’s Shift in Family Planning Policies: Concerns and Challenges

Iran’s significant success in implementing Family Planning (FP) during the past 25 years, has made it a role model in the world. The Total Fertility Rate (TFR) in Iran has dropped from 6.5 in 1960 to 1.6 in 2012, which is well below the targeted value of 2.2 for the country. Iran’s success story, however, did not merely root in the implementation of FP programs. In other words, families’ strong...

Learning Reactive Policies for Probabilistic Planning Domains

We present a planning system for selecting policies in probabilistic planning domains. Our system is based on a variant of approximate policy iteration that combines inductive machine learning and simulation to perform policy improvement. Given a planning domain, the system iteratively improves the best policy found so far until no more improvement is observed or a time limit is exceeded. Thoug...

Using Learned Policies in Heuristic-Search Planning

Many current state-of-the-art planners rely on forward heuristic search. The success of such search typically depends on heuristic distance-to-the-goal estimates derived from the plangraph. Such estimates are effective in guiding search for many domains, but there remain many other domains where current heuristics are inadequate to guide forward search effectively. In some of these domains, it ...