Many robotics applications and scenarios that involve interaction with humans are safety or performance critical. A natural path to assessing such notions is to include a cognitive model describing typical human behaviors into a larger modeling context. In this work, we set out to investigate a combination of such a model with formal verification. We present a general and flexible framework utilizing methods from probabilistic model checking and discuss current pitfalls. We start from information about typical behavior, obtained from generalizing specific scenarios by the usage of inverse reinforcement learning. We translate this information in order to define a formal model exhibiting stochastic behavior (whenever significant data is present) or nondeterminism (if the model is underspecified or no significant data is present) that can be analyzed. This model for a human can be combined with a robot model by using standard parallel composition. The benefit is manyfold: First, safe or optimal strategies for involved robots regarding a human can be synthesized depending on the given model. In general, verification can determine if such benign strategies are even possible. Furthermore, the cognitive model itself can be analyzed with respect to possible unnatural behaviors; thereby feedback to developers of such models is provided. We evaluate and describe our approaches by means of a well-known model for visiomotor tasks and provide a framework that can readily incorporate other models.

A Bot monitoring tool that will capture user questions to the bot and send them to a slack channel. Any time, the bot does not have an answer, it will ping a human in slack and then the human will provide the answer to the bot. In turn, the bot will respond to the user.

Machine learning is on the rise due to the technological convergence of the growth of big data, decreasing data storage costs, increasing computing power, improved artificial intelligence algorithms and acceleration of cloud computing. Machine learning is the ability for computers to learn without explicit programming. It's analogous to the human ability to identify an octopus based on the set of data input that goes to the brain, such as eight arms, tentacles, lack of skeleton and other characteristics, without having prior knowledge of every type of cephalopod mollusk in existence. However, human decision-making is subject to numerous cognitive biases that can easily distort judgement. For example, iconoclastic author Tom Peters highlights 159 cognitive biases that impact management decision-making (Peters, Tom.

Every day, millions of people gather on online game servers to collaborate in real time toward shared goals. What may seem like frivolous activity may, when investigated more closely, provide revolutionary opportunities to advance the science of teamwork. Teamwork is an important part of modern society, however, collaboration between people is often made difficult due to differing ideals, opinions, and personality types. We leverage a popular self-hosted multiplayer online game environment to design a framework for developing and deploying tasks that elicit different kinds of teamwork. We propose to use these to capture fine-scale details of individual and group performance across environments. The game in which we implement this system, Minecraft, is ideal because it is heavily modifiable and already enjoys a vast user base of surprising gender, age, and ethnic diversity. We heavily modify the game in order to introduce new mechanics that facilitate collaboration, monitor activity, and manipulate group composition, all to provide the groundwork for deeper quantitative insights into effective teams.

I've spent many years referencing Wikipedia's list of cognitive biases whenever I have a hunch that a certain type of thinking is an official bias but I can't recall the name or details. It's been an invaluable reference for helping me identify the hidden flaws in my own thinking. Nothing else I've come across seems to be both as comprehensive and as succinct. However, honestly, the Wikipedia page is a bit of a tangled mess. Despite trying to absorb the information of this page many times over the years, very little of it seems to stick.

Using humanoid robots to study human behavior.

According to a tweet from Newman, sales increased by 74 percent when the update was released and overall player count nearly doubled for a period, "which is the opposite of what many said would happen," he tweeted. Typically when updates roll out, like last year's that introduced randomly assigned skin color and faces, there'd be a slow but steady increase over months, rather than a sudden uptick. Newman tells Kotaku that outside of Steam sales, he's never witnessed these types of numbers before.

Somewhere in the middle of the night in a Central African rainforest, a chimpanzee gives birth. Soon after, as the sun rises, mother and newborn sit there, dazed, amid a coffee klatch of friends and relatives. Inevitably, at some point, virtually every member of the group will come over, pull the kid's legs apart and sniff: Boy or girl? It's the most binary question in biology, producing an answer that is set in stone. Biologists have long known about exceptions to the boring, staid notion that organisms are, and remain, either female or male. Now our culture is inching toward recognizing that the permanent, cleanly binary nature of gender is incorrect.

Facepunch is aware that some players don't enjoy the idea of playing Rust as a woman, creator Garry Newman says. He shared one recent player complaint on Twitter that reads, in part, "I got a dirty woman ... and everytime [sic] I see her I wanna threw [sic] up." "We understand this causes you distress and makes you not want to play the game anymore," Newman says. "Technically nothing has changed, since half the population was already living with those feelings. The only difference is that whether you feel like this is now decided by your SteamID instead of your real life gender." Facepunch is continually updating Rust and this isn't the end of the character model changes.

A mixed reality project is researching how humans interact with virtual humans over smartphones and video-based chat services. Characters created by USC's ICT Mixed Reality Lab have realistic voices and faces which can nod and smile to help the user feel like they are engaging in the conversation. The aim of the project is to improve the persuasiveness of artificial intelligence and help humans to be better guided by robots into making decisions.