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Monthly Downloads: 9
Programming language: Haskell
License: BSD 3-clause "New" or "Revised" License
Tags: AI     Games    
Latest version: v1.2.1

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smarties

Smarties is a general purpose behavior tree (BT) library written in Haskell. The library supports utility AI for advanced decision making. Smarties implements many of the design patterns outlined in this paper and some that aren't.

BTs are written in a DSL built with the NodeSequence monad. Monadic return values are used for computing utility and passing state between nodes.

To jump right in, please see the this tutorial example implementing Conway's Game of Life. There are other examples in the examples folder that I either put in too little or too much effort.

Terminology

  • perception: input and computation state of the BT. Named perception because it represents how the tree perceives the outside world. It's possible to write nodes that modify perception so that your BT has mutable perception (or state). Since you are already writing in Haskell, you probably don't ever want to do this.
  • sequence: control node that executes each child node in sequence until it hits a FAIL node and collects all output.
  • selector: control node that executes the first SUCCESS node.
  • utility: optional monadic output for a node that can be used for more complex control flow. For example utilitySelector executes the node that has the largest utility.

Understanding the NodeSequence Monad

NodeSequence is a computation that executes all it's internal nodes. At each >>= it will check the output and early exit if it reaches a FAIL.

NodeSequence has the following definition

data NodeSequenceT g p o m a =  NodeSequence { runNodeSequenceT :: g -> p -> (a, g, p, Status, [o]) }

The sequence represents a computation that takes a generator and perception and returns an output with the following types:

  • a: monad output type, typically used for computing utility
  • g: random generator
  • p: perception type
  • Status: Status of executing NodeSequence, either SUCCESS or FAIL
  • o: output type (or action type)

NodeSequence looks a lot like StateT (p,g) Writer [o] except with an additional Status output. The difference is that with each >>= if the input computation has Status FAIL, the monad will stop accumulating changes on p and appending to [o]. Note that it will continue to pass through p and g to evaluate the monadic return value a which is needed for things like utility selectors. Thus running NodeSequence produces an a and two thunks representing the perception and output up until the first FAIL.

The monadic return value is useful for passing general information between nodes. For example it's possible to implement loops:

howQueerIsMyFriend = sequence $ do
    x <- getFriend
    n <- numberFriendsOf x
    clique <- forM [0..(n-1)] (\n' -> do
        s <- getFriendOf x n'
        return queerness s
        )
    return (mean clique)

Builders

Smarties provides the Smarties.Builders module for building your own logic nodes which are needed to actually use Smarties in a project. It supports the following types of nodes:

  • Condition: create a condition node
  • Action: create an action node
  • Utility: create a node that returns a utility score
  • Perception: create a node that modify the perception

Each builder (except for Perception) has a simple variant (prefixed by Simple) which ensures the perception is immutable. You'll want to use the simple variants in most cases.

To keep the syntax simple in most cases, there are non-transformer variants of each builder which wrap the transformer ones.

Other

  • Smarties gives access to the (rather simple) BT control methods in Smarties.Nodes. Most of its power comes from the flexibility of monadic syntax. In some cases, it may be better/simpler to use something like StateT (p,g) Writer [o]. Sequence and selectors are still possible with monadic operations like ifM.

Additional Features:

Some ideas for features to add to this package. I'll probably never get to these but feel free to submit a PR.

  • Built in support for Statistic.Distribution.Normal for modeling risk reward. This includes basic operations on distributions.

  • It is possible to modify perception during tree execution. This is only recommended in the special case where the input state is same as what the tree is operating on as a whole in which case the tree represents a sequential set of operations on a value. e.g. NodeSequence g Int (Int->Int) represents operations on an Int value. In these cases, ensure the SelfActionable p o constraint is satisfied and use SelfAction which is the same as Action except also applies the output to the perception. The current implementation is a little idiosyncratic and I may remove in the future so it's mentioned here for now.