A hallmark of human intelligence is the ability to ask rich, creative, and revealing questions. Here we introduce a cognitive model capable of constructing humanlike questions. Our approach treats questions as formal programs that, when executed on the state of the world, output an answer. The model specifies a probability distribution over a complex, compositional space of programs, favoring concise programs that help the agent learn in the current context. We evaluate our approach by modeling the types of open-ended questions generated by humans who were attempting to learn about an ambiguous situation in a game. We find that our model predicts what questions people will ask, and can creatively produce novel questions that were not present in the training set. In addition, we compare a number of model variants, finding that both question informativeness and complexity are important for producing human-like questions.

This paper presents Tinker Tales, an interactive storytelling framework in the format of a board game, designed to support both narrative development and AI literacy in early childhood. The framework integrates tangible and speech-based interactions with AI through NFC chip-attached pawns and tokens, along with a speaker and microphone. Children select and define key story elements-such as characters, places, items, and emotions-using the pawns and tokens, providing further details to the AI and receiving proper assistance, similar to how adults prompt AI for specific tasks (e.g., writing). For evaluation, several game sessions were simulated with a child AI agent, and the quality and safety of the generated stories were assessed from various perspectives. This work highlights the potential of combining physical and digital elements in AI literacy, offering a safe and engaging way for children to learn how to effectively collaborate with AI.

If you've ever battled crowds to get a coveted toy you know that early October is the perfect time for parents to start holiday shopping. Amazon knows this, which is why the company is holding a second Prime Day sale event--which ends tonight--featuring some great Prime Day toy deals. You can find all the best Prime Day deals here. But if your kids are like my kids, they're already working on their wish lists. If you say you haven't already started budgeting, you are either lying, financially irresponsible, or your children are much less demanding than mine are (I know, it's my fault).

A hallmark of human intelligence is the ability to ask rich, creative, and revealing questions. Here we introduce a cognitive model capable of constructing humanlike questions. Our approach treats questions as formal programs that, when executed on the state of the world, output an answer. The model specifies a probability distribution over a complex, compositional space of programs, favoring concise programs that help the agent learn in the current context. We evaluate our approach by modeling the types of open-ended questions generated by humans who were attempting to learn about an ambiguous situation in a game. We find that our model predicts what questions people will ask, and can creatively produce novel questions that were not present in the training set. In addition, we compare a number of model variants, finding that both question informativeness and complexity are important for producing human-like questions.

Software agents have emerged as promising tools for addressing complex software engineering tasks. Existing works, on the other hand, frequently oversimplify software development workflows, despite the fact that such workflows are typically more complex in the real world. Thus, we propose AgileCoder, a multi agent system that integrates Agile Methodology (AM) into the framework. This system assigns specific AM roles - such as Product Manager, Developer, and Tester to different agents, who then collaboratively develop software based on user inputs. AgileCoder enhances development efficiency by organizing work into sprints, focusing on incrementally developing software through sprints. Additionally, we introduce Dynamic Code Graph Generator, a module that creates a Code Dependency Graph dynamically as updates are made to the codebase. This allows agents to better comprehend the codebase, leading to more precise code generation and modifications throughout the software development process. AgileCoder surpasses existing benchmarks, like ChatDev and MetaGPT, establishing a new standard and showcasing the capabilities of multi agent systems in advanced software engineering environments.

Large language models show impressive capabilities. Are they just superficial statistics – or is there more to them? Systems such as OpenAI's GPT-3 have shown that large language models have capabilities that can make them useful tools in areas as diverse as text processing and programming. With ChatGPT the company has released a model that puts these capabilities in the hands of the general public, creating new challenges for educational institutions, for example. Impressive capabilities quickly lead to the overestimation of AI systems like ChatGPT.

"Learning about the social implications of the technology you're working on is really important," says senior Austen Roberson. Austen Roberson's favorite class at MIT is 2.S007 (Design and Manufacturing I-Autonomous Machines), in which students design, build, and program a fully autonomous robot to accomplish tasks laid out on a themed game board. "The best thing about that class is everyone had a different idea," says Roberson. "We all had the same game board and the same instructions given to us, but the robots that came out of people's minds were so different." The game board was Mars-themed, with a model shuttle that could be lifted to score points.

Many senses, smell, touch, hearing, and sight, can potentially be augmented, though the most common application of AR is sight, using a head-mounted display [2]. Several users may simultaneously access and operate a shared digitally augmented environment, either at the same place or remotely. Users commonly interact with each other and the augmented elements in this virtual framework by using hand gestures, movement, and even gaze. The interactive nature of AR, as well as its direct connection to the real world, have produced extensive research work and industrial applications of AR to different fields such as education, entertainment, medicine, and retail [6]. Human-Computer interaction in games (HCI-games) is a very broad field that covers research on the many ways in which human players interact with digital games that, given their interactive, playful, and challenging nature, present a rich field of study separated from human-computer interaction in other forms of software [1].

The Minimax algorithm, also known as MinMax, is a popular algorithm for calculating the best possible move a player can player in a zero-sume game, like Tic-Tac-Toe or Chess. It makes use of an evaluation-function provided by the developer to analyze a given game board. During the execution Minimax builds a game tree that might become quite large. This causes a very long runtime for the algorithm. In this article I'd like to introduce 10 methods to improve the performance of the Minimax algorithm and to optimize its runtime.

Game boards are described in the Ludii general game system by their underlying graphs, based on tiling, shape and graph operators, with the automatic detection of important properties such as topological relationships between graph elements, directions and radial step sequences. This approach allows most conceivable game boards to be described simply and succinctly.