For those with a modest automated planning background, F.E.A.R. is inspirational because it shows even the most ambitious and inefficient planners, i.e. STRIPS,

For those with a modest automated planning background, F.E.A.R. is inspirational because it shows even the most ambitious and inefficient planners, i.e. STRIPS, which is the grand-daddy of automated planning that searches across flat goals based on a known global state... even that, which is known to be an NP-complete problem in many domains, can be made practical in specific domains with tweaks. Much of the automated planning literature is about finding ways to find the balance between computational tractability and expressivity of the domain & goals. Many attempts to fuse logic and imperative programming with an aspect of history: hierarchical task networks for example, which are like a high level procedural language with non-deterministic method dispatch. Games like Killzone 2 for the PS3 also had an interesting followup use of automated planning via hierarchical task networks, to do squad tactics; http://aigamedev.com/open/coverage/htn-planning-discussion/ http://aigamedev.com/open/coverage/htn-planning-discussion/ Another thread is Golog as a variant of Prolog that uses Reiter's Situation Calculus to formulate complex goals and domain constraints in a relatively consistent syntax and applied towards "Cognitive Robotics": http://www.cs.toronto.edu/cogrobo/main/systems/ http://www.cs.toronto.edu/cogrobo/main/systems/ .. it's unfortunate a lot of that work seems stalled since Reiter passed away. With all the recent hype around connectionist, deep learning AI, etc., there still is a lot of important work going on in the symbolic logic side of AI, particularly in this space of working in dynamical domains. Fusing the two will be really interesting.