{"push_id":3111224141,"size":1,"distinct_size":1,"ref":"refs/heads/master","head":"ffc9838e03655f542dfa9331293a0415a28d2f96","before":"527117c1a2df81343c14bac54354ebfc2f5afb09","commits":[{"sha":"ffc9838e03655f542dfa9331293a0415a28d2f96","author":{"name":"Kyle Topasna","email":"fe9299738f8e0a61db1f44e2c652310823f891ff@gmail.com"},"message":"Implemented inefficient form of h_levelsum\n\nImplemented the h_levelsum function in it's\ninefficient form. The current function fills in the\ngraph before calculating the heuristic.\n\nThe level sum is the sum of the level costs of all the goal literals\n        combined. The \"level cost\" to achieve any single goal literal is the\n        level at which the literal first appears in the planning graph. Note\n        that the level cost is **NOT** the minimum number of actions to\n        achieve a single goal literal.\n\n        For example, if Goal_1 first appears in level 0 of the graph (i.e.,\n        it is satisfied at the root of the planning graph) and Goal_2 first\n        appears in level 3, then the levelsum is 0 + 3 = 3.\n\nTODO: Reimplement to improve efficiency by calculating the sum during graph\nexpansion.","distinct":true,"url":"https://api.github.com/repos/alekzandr/ai-nanodegree/commits/ffc9838e03655f542dfa9331293a0415a28d2f96"}]}
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