In the fall of 2015, celebrated visual effects whiz Pablo Helman was in Taiwan celebrating Thanksgiving with Martin Scorsese. The 24-year veteran of Industrial Light & Magic, the company founded by George Lucas at the onset of the Star Wars franchise, was midway through production on the director's Jesuit missionary saga, Silence, for which Helman had to digitally re-create the enormity of St. Paul's College of Macau. But over holiday dinner, Scorsese began pitching Helman on a different film entirely. It was another adaption, this one based on I Heard You Paint Houses, Charles Brandt's biography of mob hit man and supposed Jimmy Hoffa murderer Frank Sheeran. Much like Silence, the story was expansive, though instead of spanning geography (Portugal to Japan), the movie would stretch across years (approximately seven decades).

Adapting plans to changes in the environment by finding alternatives and taking advantage of opportunities is a common human behavior. The need for such behavior is often rooted in the uncertainty produced by our incomplete knowledge of the environment. While several existing planning approaches deal with such issues, artificial agents still lack the robustness that humans display in accomplishing their tasks. In this work, we address this brittleness by combining Hierarchical Task Network planning, Description Logics, and the notions of affordances and conceptual similarity. The approach allows a domestic service robot to find ways to get a job done by making substitutions. We show how knowledge is modeled, how the reasoning process is used to create a constrained planning problem, and how the system handles cases where plan generation fails due to missing/unavailable objects. The results of the evaluation for two tasks in a domestic service domain show the viability of the approach in finding and making the appropriate goal transformations.