In decision-making scenarios, \textit{reasoning} can be viewed as an algorithm $P$ that makes a choice of an action $a^* \in \mathcal{A}$, aiming to optimize some outcome such as maximizing the value function of a Markov decision process (MDP). However, executing $P$ itself may bear some costs (time, energy, limited capacity, etc.) and needs to be considered alongside explicit utility obtained by making the choice in the underlying decision problem. Such costs need to be taken into account in order to accurately model human behavior, as well as optimizing AI planning, as all physical systems are bound to face resource constraints. Finding the right $P$ can itself be framed as an optimization problem over the space of reasoning processes $P$, generally referred to as \textit{metareasoning}. Conventionally, human metareasoning models assume that the agent knows the transition and reward distributions of the underlying MDP. This paper generalizes such models by proposing a meta Bayes-Adaptive MDP (meta-BAMDP) framework to handle metareasoning in environments with unknown reward/transition distributions, which encompasses a far larger and more realistic set of planning problems that humans and AI systems face. As a first step, we apply the framework to two-armed Bernoulli bandit (TABB) tasks, which have often been used to study human decision making. Owing to the meta problem's complexity, our solutions are necessarily approximate, but nevertheless robust within a range of assumptions that are arguably realistic for human decision-making scenarios. These results offer a normative framework for understanding human exploration under cognitive constraints. This integration of Bayesian adaptive strategies with metareasoning enriches both the theoretical landscape of decision-making research and practical applications in designing AI systems that plan under uncertainty and resource constraints.

In light of the recent events surrounding COVID-19, in order to make it easier for K-12 students to learn and be productive, a team led by Media Lab Associate Professor Cynthia Breazeal has launched a website to share a variety of online activities to learn about artificial intelligence. Due to this COVID-19 pandemic, parents and educators must be feeling overwhelmed in providing K-12 students classroom level education, and that's why the team decided to create this website consisting of MIT resources to learn about designing artificial intelligence and its responsible uses. The website has been created in collaboration between the Media Lab, MIT Stephen A. Schwarzman College of Computing, and MIT Open Learning. It acts as a hub to highlight diverse work by faculty, staff, and students across the MIT community at the intersection of AI, learning, and education. According to the team, it will be interesting for K-12 students to learn from this website.

In light of the recent events surrounding Covid-19, learning for grades K-12 looks very different than it did a month ago. Parents and educators may be feeling overwhelmed about turning their homes into classrooms. With that in mind, a team led by Media Lab Associate Professor Cynthia Breazeal has launched aieducation.mit.edu to share a variety of online activities for K-12 students to learn about artificial intelligence, with a focus on how to design and use it responsibly. Learning resources provided on this website can help to address the needs of the millions of children, parents, and educators worldwide who are staying at home due to school closures caused by Covid-19, and are looking for free educational activities that support project-based STEM learning in an exciting and innovative area. The website is a collaboration between the Media Lab, MIT Stephen A. Schwarzman College of Computing, and MIT Open Learning, serving as a hub to highlight diverse work by faculty, staff, and students across the MIT community at the intersection of AI, learning, and education. "MIT is the birthplace of Constructionism under Seymour Papert.

In this paper we outline a general approach to the study of problem-solving, in which search steps are considered decisions in the same sense as actions in the world. Unlike other metrics in the literature, the value of a search step is defined as a real utility rather than as a quasiutility, and can therefore be computed directly from a model of the base-level problem-solver. We develop a formula for the expected value of a search step in a game-playing context using the single-step assumption, namely that a computation step can be evaluated as it was the last to be taken. We prove some metalevel theorems that enable the development of a low-overhead algorithm, MGSS*, that chooses search steps in order of highest estimated utility. Although we show that the single-step assumption is untenable in general, a program implemented for the game of Othello soundly beats an alpha-beta search while expanding significantly fewer nodes, even though both programs use the same evaluation function.