Constructing personalized and anthropomorphic agents holds significant importance in the simulation of social networks. However, there are still two key problems in existing works: the agent possesses world knowledge that does not belong to its personas, and it cannot eliminate the interference of diverse persona information on current actions, which reduces the personalization and anthropomorphism of the agent. To solve the above problems, we construct the social media agent based on personalized knowledge and dynamic persona information. For personalized knowledge, we add external knowledge sources and match them with the persona information of agents, thereby giving the agent personalized world knowledge. For dynamic persona information, we use current action information to internally retrieve the persona information of the agent, thereby reducing the interference of diverse persona information on the current action. To make the agent suitable for social media, we design five basic modules for it: persona, planning, action, memory and reflection. To provide an interaction and verification environment for the agent, we build a social media simulation sandbox. In the experimental verification, automatic and human evaluations demonstrated the effectiveness of the agent we constructed.

Dogs may be our best friends, but they're also our hard-working colleagues -- tasked with everything from guarding our homes to guiding visually impaired people to sniffing out bombs. And now researchers have enlisted the help of an Alaskan Malamute named Kelp to develop an artificial intelligence system that thinks just like a dog, in hopes of creating canine-like robots. To build a database of dog behavior, a team of scientists led by Kiana Ehsani, a Ph.D. student at the University of Washington, attached sensors to Kelp's paws, torso, and tail to capture her movements for a couple of hours a day while eating, playing fetch, and walking around in various indoor and outdoor environments. A camera affixed to Kelp's head recorded what she saw as she went about her everyday activities. Over the course of several weeks, the researchers amassed more than 24,000 video frames -- all associated with particular body movements.

A team of researchers from the University of Washington and the Allen Institute for AI has trained an AI system to respond like a dog using data from an actual animal. In their paper uploaded to the arXiv preprint server, the group describes their system and what it can and cannot do. The team is also going to present their work at the Conference on Computer Vision and Pattern Recognition this summer. AI systems are typically based on deep-learning algorithms that process data describing events, and then using what they have learned to predict future behavior. In this new effort, the researchers have applied such a strategy to dog behavior.

All dog owners can testify to the powerful intelligence of their four-legged friends. Indeed, many dogs provide important services, such as guiding people who are visually impaired, finding lost individuals, or sniffing out drugs and other contraband. These abilities are beyond even the most powerful artificial intelligence. And yet AI researchers have yet to take advantage of them in training AI systems to be more capable. Today that changes thanks to the work of Kiana Ehsani at the University of Washington in Seattle and colleagues, who have gathered a unique data set of canine behavior and used it to train an AI system to make dog-like decisions.