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We see the field of metareasoning to be the answer to many large organizational problems encountered when putting together an understandable cognitive architecture, capable of commonsense reasoning. In this paper we review the EM1 implementation of the Emotion Machine critic-selector architecture, as well as explain the current progress we have made in redesigning this first version implementation. For this purpose of redesign and large-scale implementation, we have written a novel programming language, Funk2, that focuses on efficient metareasoning and procedural reflection, the keystones of the critic-selector architecture. We present an argument for why the Funk2 programming language lends itself to easing the burden on programmers that prefer to not be restricted to strictly declarative programming paradigms by allowing the learning of critic and selector activation strengths by credit assignment through arbitrary procedural code. I. CLOSED-LOOP CONTROL AND LEARNINGThere are many artificial intelligence algorithms that provide explanations for how to accomplish goals or gather rewards in a domain. A basic artificial intelligence system consists of three processes: (1) perceptual data are generalized and categorized to learn induced abstract models, (2) abstract models are used to infer expected hypothetical states, i.e. states of future, past, or otherwise "hidden" variables, (3) actions are chosen based on considerations of different action-dependent inferences.While there are many types of machine learning algorithms that focus on this abstract 3-step closed-loop process of learning to control, the field of meta-cognition [1] focuses on making at least two layers of closed-loop systems. The first closed-loop learning algorithm learns how to deal with the external world, while the second closed-loop learning algorithm perceives the state of the algorithm below. We see meta-cognition as a layering of learning algorithms, such that the second layer algorithm learns from perceiving the activity of the first layer and controls or modifies this first layer. While it may be clear how to trace changes in the perceptual inputs of layer one of the algorithm, it is less than clear how the second layer learner should monitor the changes in the state of the first layer learner. II. A REVIEW OF THE EMOTION MACHINE V1.0One system that implements commonsense reasoning, based on Minsky's Emotion Machine theory of mind [2], is a metareasoning system for correcting faulty plans, called EM1 (Emotion Machine, v1) [3]. EM1 is written in Lisp, using a Prolog extension as the logical resolution tool. EM1 is a layered architecture consisting of reactive, deliberative, and reflective layers. Mental critics are represented as commonsense narratives that result in queries to a collection of different Prolog knowledge bases. The commonsense narratives are given to the system in a Lisp format that is compiled into the knowledge bases as collections of horn clauses. These knowledge bases consist of collections of domain-specific horn cla...