There's always been something so annoying about people who found the need to stack additional challenges onto solving a Rubik's Cube quickly, whether it was doing it blind-folded or while juggling or one-handed. While it might have just been a challenge for them, it also seemed like a need to show off. OpenAI is clearly interested in showing off what its Dactyl robotic-hand can do with a Rubik's Cube. The organization announced that the robot has learned to solve a Rubik's Cube one-handed, an accomplishment that speaks to the robot's dexterity in handling and manipulating the cube more than anything. Previously, we had seen the robot interact with unknown objects without any real-world training, only virtual simulations.
For seven years, AI researchers have been struggling with an unusual challenge: shooting cartoon birds at cartoon pigs. An annual competition tests their ability to craft an AI agent that can play the popular video game Angry Birds. This month two researchers posted a paper on arXiv.org It's an example of the kind of weird obstacles that all AI researchers face as they attempt to adapt cutting-edge technologies to some very human endeavors. Teams around the world are tackling much more sophisticated problems, persevering to overcome the obstacles on the path to our shiny technology-enhanced future.
I've been interested in artificial intelligence (AI) – the concept that humans can design a machine that thinks for itself without explicitly provide instructions (or a "program") -- since the early 1970s. As an undergraduate math major, I wrote a program designed to learn to play the game of Monopoly (checkers was too easy, chess was too hard). The program "knew" the objective, the layout of the board, the rules of play, and the content of the three stacks of cards that drive the game. It could handle up to 8 players. It wasn't very smart, but it had one great advantage -- it could play thousands of games against itself, remember the outcomes, and analyze why it had won (someone always wins) or lost using some simple algorithms.
Multi-agent decision problems can often be formulated as extensive-form games. We focus on imperfect information extensive-form games in which one or more actions at many decision points have an associated continuous or many-valued parameter. A stock trading agent, in addition to deciding whether to buy or not, must decide how much to buy. In no-limit poker, in addition to selecting a probability for each action, the agent must decide how much to bet for each betting action. Selecting values for these parameters makes these games extremely large. Two-player no-limit Texas Hold'em poker with stacks of 500 big blinds has approximately 1071 states, which is more than 1050 times more states than two-player limit Texas Hold'em. The main contribution of this paper is a technique that abstracts a game's action space by selecting one, or a small number, of the many values for each parameter. We show that strategies computed using this new algorithm for no-limit Leduc poker exhibit significant utility gains over epsilon-Nash equilibrium strategies computed with standard, hand-crafted parameter value abstractions.
We study robot construction problems where multiple autonomous robots rearrange stacks of prefabricated blocks to build stable structures. These problems are challenging due to ramifications of actions, true concurrency, and requirements of supportedness of blocks by other blocks and stability of the structure at all times. We propose a formal hybrid planning framework to solve a wide range of robot construction problems, based on Answer Set Programming. This framework not only decides for a stable final configuration of the structure, but also computes the order of manipulation tasks for multiple autonomous robots to build the structure from an initial configuration, while simultaneously ensuring the stability, supportedness and other desired properties of the partial construction at each step of the plan. We prove the soundness and completeness of our formal method with respect to these properties. We introduce a set of challenging robot construction benchmark instances, including bridge building and stack overhanging scenarios, discuss the usefulness of our framework over these instances, and demonstrate the applicability of our method using a bimanual Baxter robot.