AI acting systems, or actors – which may be embodied in physical devices such as robots or in abstract procedures such as web-based service agents – require several cognitive functions, three of which are acting, planning, and learning. Acting is more than just the sensory-motor execution of low-level commands: there is a need to decide how to perform a task, given the context and changes in the environment. Planning involves choosing and organizing actions that can achieve a goal and is done using abstract actions that the agent will need to decide how to perform. Learning is important for acquiring knowledge about expected effects, which actions to perform and how to perform them, and how to plan; and acting and planning can be used to aid learning. This chapter introduces the scientific and technical challenges of developing these three cognitive functions and the ethical challenge of doing such development responsibly.

In this chapter, we propose different approaches to planning with nondeterministic models. We describe three techniques for planning with nondeterministic state transition systems: And/Or graph search (Section 12.1), planning based on determinization techniques (Section 12.2), and planning via symbolic model checking (Section 12.3). We then present techniques for planning by synthesis of input/output automata (Section 12.4). We finally briefly discuss techniques for behavior tree generation (Section 12.5).

This chapter is about planning with hierarchical refinement methods. A plan guides the acting engine RAE with informed choices about the best methods for the task and context at hand. We consider an optimizing planner to find methods maximizing a utility function. In principle, the planner may rely on an exact dynamic programming optimization procedure. An approximation approach is more adapted to the online guidance of an actor. We describe a Monte Carlo tree search planner, called UPOM, parameterized for rollout depth and number of rollouts. It relies on a heuristic function for estimating the remainder of a rollout when the depth is bounded. UPOM is an anytime planner used in a receding horizon manner. This chapter relies on chapters 8, 9, and 14. It presents refinement planning domains and outlines the approach. Section 15.2 proposes utility functions and an optimization procedure. The planner is developed in Section 15.3.

This chapter is about representing state-transition systems and using them in acting. The first section gives formal definitions of state-transition systems and planning problems, and a simple acting algorithm. The second section describes state-variable representations of state-transition systems, and the third section describes several acting procedures that use this representation. The fourth section describes classical representation, an alternative to state-variable representation that is often used in the AI planning literature.

The chapters in Part I are about acting, planning, and learning using deterministic state-transition (or "classical planning") models. The relative ease of constructing and using such models can make them desirable even though most real-world environments do not satisfy all of their underlying assumptions. The chapters in this part also introduce several concepts that will be used throughout the book, such as state-variable representation.

This part of the book is about planning, acting, and learning approaches in which time is explicit. It describes several algorithms and methods for handling durative and concurrent activities with respect to a predicted dynamics. Acting with temporal models raises dispatching and temporal controllability issues that rely heavily on planning concepts.

Nondeterministic models, like probabilistic models (see Part III), drop the assumption that an action applied in a state leads to only one state. The main difference with probabilistic models is that nondeterministic models do not have information about the probability distribution of transitions. In spite of this, the main motivation for acting, planning, and learning using nondeterministic models is the same as that of probabilistic approaches, namely, the need to model uncertainty: most often, the future is never entirely predictable without uncertainty. Nondeterministic models might be thought to be a special case of probabilistic models with a uniform probability distribution. This is not the case. In nondeterministic models we do not know that the probability distribution is uniform; we simply do not have any information about the distribution.

HTN planning algorithms require a set of HTN methods that provide knowledge about potential problem-solving strategies. Typically these methods are written by a domain expert, but this chapter is about some ways to learn HTN methods from examples. It describes how to learn HTN methods in learning-by-demonstration situations in which a learner is given examples of plans for various tasks, and also in situations where the learner is given only the plans and must infer what tasks the plans accomplish. The chapter also speculates briefly about prospects for a “planning-to-learn” approach in which a learner generates its own examples using a classical planner.