Although even propositional STRIPS planning is a hard problem in general, many instances of the problem, including many of those commonly used as benchmarks, are easy. In spite of this, they are often hard to solve for domain-independent planners, because the encoding of the problem into a general problem specification formalism such as STRIPS hides structure that needs to be exploited to solve...

Inverse reinforcement learning (IRL) attempts to infer human rewards or preferences from observed behavior. However, human planning systematically deviates from rationality. Though there has been some IRL work which assumes humans are noisily rational, there has been little analysis of the general problem of inferring the reward of a human of unknown rationality. The observed behavior can, in p...

Many planning applications must address conflicting plan objectives, such as cost, risk, duration, and resource consumption and decision makers want to know the possible trade-offs. Traditionally, such problems are solved by invoking a single-objective algorithm (such as A*) on multiple, alternative preferences of the objectives to identify non-dominated plans. The less-popular alternative is t...

Many observers see the world as having “problems” that cry out for “solutions”. Yet, more than one wise man has noted that there are no “solutions”, only trade-offs. Looked at in this way, many of the “solutions” that fall under the heading “sustainability planning” must be carefully reconsidered. There is also good news, however. Much of humanity has reached heights of material welfare that we...

Task-based instruction is arguably associated with fostering fluent L2 speech distant from native-like accuracy. Of several methodological options recommended for accounting for accuracy problems of meaning-first approaches to language teaching, planning time has been explored in this study. Three groups of English majors watched a cartoon and narrated their accounts of watching under no-planni...