Adaptation-Based Programming in Haskell

TL;DR

This paper introduces an embedded domain-specific language in Haskell for adaptation-based programming, enabling the creation of adaptive, self-optimizing programs that respond to feedback and support reinforcement learning.

Contribution

It presents a novel Haskell DSL for ABP, leveraging type classes to facilitate compositional adaptive constructs and support reinforcement learning functionalities.

Findings

Supports adaptive values that respond to feedback

Enables self-optimization through repeated executions

Facilitates reinforcement learning integration

Abstract

We present an embedded DSL to support adaptation-based programming (ABP) in Haskell. ABP is an abstract model for defining adaptive values, called adaptives, which adapt in response to some associated feedback. We show how our design choices in Haskell motivate higher-level combinators and constructs and help us derive more complicated compositional adaptives. We also show an important specialization of ABP is in support of reinforcement learning constructs, which optimize adaptive values based on a programmer-specified objective function. This permits ABP users to easily define adaptive values that express uncertainty anywhere in their programs. Over repeated executions, these adaptive values adjust to more efficient ones and enable the user's programs to self optimize. The design of our DSL depends significantly on the use of type classes. We will illustrate, along with presenting our DSL, how the use of type classes can support the gradual evolution of DSLs.