What is AI Planning?
AI Planning is a field of Artificial Intelligence which explores the process of using autonomous techniques to solve planning and scheduling problems. A planning problem is one in which we have some initial starting state, which we wish to transform into a desired goal state through the application of a set of actions.
In a simple example, imagine we wish to build a house. On our building site we can assume all the materials are ready to be used and we essentially now have to decide which order to perform construction in. Each step of construction in this situation could be modelled as an action (for example laying foundations, building the walls, installing the plumbing and electricals etc.).
Certain actions cannot happen before others - we cannot build a wall before we have a foundation - so through the use of predicate logic we can express this problem by saying “if there is no foundation, we cannot build a wall”.
AI planning can leverage this form of expression in order to solve this problem and others. Of course in reality planning the construction of a house is not particularly difficult, but imagine instead we were planning the construction of an Olympic stadium. The same problem can be broken down into similar simple tasks which are dependent on prior conditions being met.
Domain Independent Planning
The core philosophy built into PDDL (Ghallab et al., 1998), and as a result many planners, is that the techniques applied to solving a problem should know nothing about the problem they are solving.
That is to say the techniques which we apply to solve for example a logistics problem, we could also apply to a manufacturing problem. This is interesting because unlike other fields of Artificial Intelligence, such as Machine Learning, we are not dependent on there being pre-existing information on the problem, or an example path to a solution. All we need is the starting state, the actions we can apply and the desired end state.
Often these three pieces of information are readily available, purely from the nature inherent to the problems a planner is trying to solve. Simply put, you don’t start planning until you know what you’re trying to achieve.
For example if we are planning the construction of large stadium, we know what needs to be completed in order for the stadium to be finished, we know that there is currently no stadium built and we know the different actions that can be performed to change the state closer to the position we desire it to be in (build frames, install pipework, raise flags etc.).
We need no knowledge of how previous stadiums were built, or what orderings were used (for example building foundations then frames etc.). This lack of need for knowledge means we can apply planning to problems which have not been previously solved.
Also, if we assume that previous solutions to a planning problem are sub-optimal (i.e. they’re not as good as they could be), applying a technique like learning would essentially train to solve problems sub-optimally. Therefore we need an appraoch which is methodical, objective and avoids learning from prior approaches which are sub-optimal (read as domain independent).
State Space Search
Although there is a lot more to solving a plan than a state space search, the general (but not universal) unifying factor of different planners is that they perform some kind of “state space” search.
If we imagine that from the initial state, we can access various new states, by applying actions, the result is we form a tree of possible states, with the initial state being the root. From that root we perform a search, attempting to find a state as close as possible to the root, which has all of the facts we need in order to satisfy the goal.
What is Planning Domain Definition Language (PDDL)?
Planning Domain Definition Language (PDDL) is a family of languages which allow us to define a planning problem. As planning has evolved, so to has the language used to describe it and as such there are now many versions of PDDL available with different levels of expressivity.
What is a planner?
An AI Planner is the other half of the proverbial planning equation. Where PDDL allows us to define an AI Planning problem, an AI Planner allows us to attempt to solve one. An AI planner reads in PDDL and uses it in order to decompose and solve the problem.
As AI Planners have evolved alongside the languages they use, different planners have different levels of support for different syntaxes. Older planners do not support more expressive syntaxes such as PDDL3.0 or PDDL+. In some cases, however, older styles of syntax have been deprecated and as such some newer planners may not support certain variants of syntax.
This means that in some cases a newer planner may not be capable of running an older problem and an older planner may not be capable of running a newer problem. In an attempt to combat this problem, as this guide evolves we’ll attempt to include notes about what functionality a broad range of planners support.
There are however a really wide array of planners and some are objectively better or worse than others either unilaterally or in solving specific instances. This guide again will focus primarily on the best general purpose planners, in order to complement the focus on general purpose syntax.
With this we have come to an end of our longest discussion that is AI.
Further we'll start an new topic that is Programming Languages from 1st Jan 2022.
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