Distributed artificial intelligence Distributed Artificial Intelligence (DAI) is a subfield of artificial intelligence research dedicated to the development of distributed solutions for complex problems regarded as requiring intelligence.DAI is closely related to and a predecessor of the field of Multi-Agent Systems.

Artificial Intelligence is all the rage amongst founders and investors. And for good reason: regardless of where you think we are in the hype cycle, it's increasingly clear AI is eventually going to touch everything. The questions now turn to when and how it will impact specific markets and categories. I've been particularly excited about the consumerization of AI and the impact on everyday products and platforms for consumers and professionals. There's a tendency to reduce AI to machine learning (ML), the subfield primarily responsible for AI's resurgence, but ML is just one part of a broader story.

In May 2016, TechRepublic challenged a startup called Unanimous A.I. to predict what some thought would be impossible: The superfecta at the Kentucky Derby. Hardly anyone, including Louis Rosenberg, CEO of Unanimous A.I., thought this would actually work--but he accepted the challenge, creating an artificial "swarm" through an AI-based platform called UNU that picked the top four horses, in order, at the 2016 Derby. The swarm consisted of a group of 20 people with some knowledge of horse racing, chosen anonymously, who participated on the UNU platform. The model, based loosely on the concept of nature's swarms--How do honeybees decide where to migrate to?--incorporated a kind of group intelligence, a collective decision. The swarm correctly predicted the superfecta, beating 540-1 odds.

Successful human-robot cooperation hinges on each agent's ability to process and exchange information about the shared environment and the task at hand. Human communication is primarily based on symbolic abstractions of object properties, rather than precise quantitative measures. A comprehensive robotic framework thus requires an integrated communication module which is able to establish a link and convert between perceptual and abstract information. The ability to interpret composite symbolic descriptions enables an autonomous agent to a) operate in unstructured and cluttered environments, in tasks which involve unmodeled or never seen before objects; and b) exploit the aggregation of multiple symbolic properties as an instance of ensemble learning, to improve identification performance even when the individual predicates encode generic information or are imprecisely grounded. We propose a discriminative probabilistic model which interprets symbolic descriptions to identify the referent object contextually w.r.t.\ the structure of the environment and other objects. The model is trained using a collected dataset of identifications, and its performance is evaluated by quantitative measures and a live demo developed on the PR2 robot platform, which integrates elements of perception, object extraction, object identification and grasping.

Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is concerned with systems that consist of multiple independent entities that interact in a domain. Traditionally, DAI has been divided into two sub-disciplines: Distributed Problem Solving (DPS) focusses on the information management aspects of systems with several branches working together towards a common goal; Multiagent Systems (MAS) deals with behavior management in collections of several independent entities, or agents. This survey of MAS is intended to serve as an introduction to the field and as an organizational framework. A series of increasingly complex general multiagent scenarios are presented.

This book constitutes the refereed proceedings of the Second International Workshop on Intelligent Agents for Telecommunication Applications, IATA'98, held in Paris, France, in July 1998, in conjunction with the 1998 Agents World Conference. The book presents 17 revised full papers carefully selected for inclusion in the volume. The book is divided into topical sections on network architecture, network configuration and planning, network optimization, network management, agent-based architectures for service applications.

In about A.D. 1300 the Anasazi people abandoned Long House Valley. To this day the valley, though beautiful in its way, seems touched by desolation. It runs eight miles more or less north to south, on the Navajo reservation in northern Arizona, just west of the broad Black Mesa and half an hour's drive south of Monument Valley. To the west Long House Valley is bounded by gently sloping domes of pink sandstone; to the east are low cliffs of yellow-white sedimentary rock crowned with a mist of windblown juniper. The valley floor is riverless and almost perfectly flat, a sea of blue-gray sagebrush and greasewood in sandy reddish soil carried in by wind and water. Today the valley is home to a modest Navajo farm, a few head of cattle, several electrical transmission towers, and not much else. Yet it is not hard to imagine the vibrant farming district that this once was. The Anasazi used to cultivate the valley floor and build their settlements on low hills around the valley's perimeter. Remains of their settlements are easy to see, even today. Because the soil is sandy and the wind blows hard, not much stays buried, so if you leave the highway and walk along the edge of the valley (which, by the way, you can't do without a Navajo permit), you frequently happen upon shards of Anasazi pottery, which was eggshell-perfect and luminously painted. On the site of the valley's eponymous Long House--the largest of the ancient settlements--several ancient stone walls remain standing. Last year I visited the valley with two University of Arizona archaeologists, George Gumerman and Jeffrey Dean, who between them have studied the area for fifty or more years. Every time I picked up a pottery shard, they dated it at a glance. By now they and other archaeologists know a great deal about the Anasazi of Long House Valley: approximately how many lived here, where their dwellings were, how much water was available to them for farming, and even (though here more guesswork is involved) approximately how much corn each acre of farmland produced. They have built up a whole prehistoric account of the people and their land. But they still do not know what everyone would most like to know, which is what happened to the Anasazi around A.D. 1300. "Really, we've been sort of spinning our wheels in the last eight to ten years," Gumerman told me during the drive up to the valley. "Even though we were getting more data, we haven't been able to answer that question."

Watson, the IBM computer system that attracted millions of viewers when it defeated two Jeopardy champions handily in 2011, is finally going to meet its public. Last week, IBM announced that a version of the artificially intelligent software that gave Watson its smarts is to be rented out to companies as a customer service agent. It will be able to respond to questions posed by people, and sustain a basic conversation by keeping track of context and history if a person asks further questions. An "Ask Watson" button on websites or mobile apps will open a text-based dialogue with the retired Jeopardy champion on topics such as product buying decisions and troubleshooting guidance. This new version of Watson, somewhat opaquely called "Watson Engagement Advisor," will be the Jeopardy champ's first truly public test.

Scientists in the Netherlands are endowing a robotic cat with a set of logical rules for emotions. They believe that by introducing emotional variables to the decision-making process, they should be able to create more-natural human and computer interactions. "We don't really believe that computers can have emotions, but we see that emotions have a certain function in human practical reasoning," says Mehdi Dastani, an artificial-intelligence researcher at Utrecht University, in the Netherlands. By bestowing intelligent agents with similar emotions, researchers hope that robots can then emulate this humanlike reasoning, he says. The hardware for the robot, called iCAT, was developed by the Dutch research firm Philips and designed to be a generic companion robotic platform. By enabling the robot to form facial expressions using its eyebrows, eyelids, mouth, and head position, the researchers are aiming to let it show if it is confused, for example, when interacting with its human user.