A framework for consensus modelling is introduced using Kleene's three valued logic as a means to express vagueness in agents' beliefs. Explicitly borderline cases are inherent to propositions involving vague concepts where sentences of a propositional language may be absolutely true, absolutely false or borderline. By exploiting these intermediate truth values, we can allow agents to adopt a more vague interpretation of underlying concepts in order to weaken their beliefs and reduce the levels of inconsistency, so as to achieve consensus. We consider a consensus combination operation which results in agents adopting the borderline truth value as a shared viewpoint if they are in direct conflict. Simulation experiments are presented which show that applying this operator to agents chosen at random (subject to a consistency threshold) from a population, with initially diverse opinions, results in convergence to a smaller set of more precise shared beliefs. Furthermore, if the choice of agents for combination is dependent on the payoff of their beliefs, this acting as a proxy for performance or usefulness, then the system converges to beliefs which, on average, have higher payoff.

By 2021, robots will have eliminated 6% of all jobs in the US, starting with customer service representatives and eventually truck and taxi drivers. That's just one cheery takeaway from a report released by market research company Forrester this week. These robots, or intelligent agents, represent a set of AI-powered systems that can understand human behavior and make decisions on our behalf. Current technologies in this field include virtual assistants like Alexa, Cortana, Siri and Google Now as well as chatbots and automated robotic systems. For now, they are quite simple, but over the next five years they will become much better at making decisions on our behalf in more complex scenarios, which will enable mass adoption of breakthroughs like self-driving cars.

I am very interested in reinforcement-settings in a multi-agent system. I have a hard time finding papers about reinforcement learning of sophisticated multi-agent settings, such as having a small society of agents which can trade goods with each other or interact in some other way. Is there even work in that direction? The "clostest" thing which interests me are predator-prey models. When searching for them I found a few interesting ones.

A recent algorithmic family for distributed optimization, DIGing's, have been shown to have geometric convergence over time-varying undirected/directed graphs. Nevertheless, an identical step-size for all agents is needed. In this paper, we study the convergence rates of the Adapt-Then-Combine (ATC) variation of the DIGing algorithm under uncoordinated step-sizes. We show that the ATC variation of DIGing algorithm converges geometrically fast even if the step-sizes are different among the agents. In addition, our analysis implies that the ATC structure can accelerate convergence compared to the distributed gradient descent (DGD) structure which has been used in the original DIGing algorithm.

Bayes's Theorem fundamentally is based on the concept of "validity of Beliefs". Reverend Thomas Bayes was a Presbyterian minster and a Mathematician who pondered much about developing the proof of existence of God. He came up with the Theorem in 18th century (which was later refined by Pierre-Simmon Laplace) to fix or establish the validity of'existing' or'previous' Beliefs in the face of best available'new' evidence. Think of it as a equation to correct prior beliefs based on new evidence. One of the popular example used to explain Bayes's Theorem is to detect if a patient has a certain disease or not.

If a given behavior of a multi-agent system restricts the phase variable to a invariant manifold, then we define a phase transition as change of physical characteristics such as speed, coordination, and structure. We define such a phase transition as splitting an underlying manifold into two sub-manifolds with distinct dimensionalities around the singularity where the phase transition physically exists. Here, we propose a method of detecting phase transitions and splitting the manifold into phase transitions free sub-manifolds. Therein, we utilize a relationship between curvature and singular value ratio of points sampled in a curve, and then extend the assertion into higher-dimensions using the shape operator. Then we attest that the same phase transition can also be approximated by singular value ratios computed locally over the data in a neighborhood on the manifold. We validate the phase transitions detection method using one particle simulation and three real world examples.

One of the biggest concerns surrounding artificial intelligence is that the technology will eliminate jobs as it becomes increasingly more sophisticated. According to a new report from Forrester Research, "intelligent agents and related robots" will eliminate 6 percent of U.S. jobs by 2021, wiping out, as The New York Post points out, 8.6 million jobs, or the equivalent of Florida's entire workforce, in just five years. And that's only the beginning of a "disruptive tidal wave" that will swallow up millions more jobs in the coming decades. "By 2021, AI within intelligent agents will evolve significantly beyond today's relatively simple machine learning and natural language processing (NLP)," reads the report. "Emerging applications will feature improved self-learning and more complex scenarios. As basic agents gain consumer adoption, next-generation AI will not power intelligent agents until 2020 or beyond."

Few trends in technology have caused the level of panic and uncertainty in the job market as artificial intelligence (AI). The impending "robot revolution" has brought questions about what jobs, if any, will be replaced by bots, and when it will happen. While some have posited that robots will replace nearly all jobs, and free up humans to work on more creative endeavors, others have been more reserved in their predictions. A new report for Forrester Research claims that, by 2021, "intelligent agents and related robots" will only have eliminated 6% of jobs. "By 2021, AI within intelligent agents will evolve significantly beyond today's relatively simple machine learning and natural language processing (NLP)," the report said.

Artificial intelligence is making fast progress by the day. A report by Forrester indicates that in the next five years, robots and intelligent agents will eliminate 6% of jobs. The intelligent agents include chat bots and digital assistants like Alexa of Amazon, Siri of Apple, GoogleNow of Alphabet, and Messenger bots of Facebook. They will find more ground in developing robots and intelligent agents which learn better from users and handle more complex scenarios than they do now.

By 2021, robots will have eliminated 6% of all jobs in the US, starting with customer service representatives and eventually truck and taxi drivers. That's just one cheery takeaway from a report released by market research company Forrester this week. These robots, or intelligent agents, represent a set of AI-powered systems that can understand human behavior and make decisions on our behalf. Current technologies in this field include virtual assistants like Alexa, Cortana, Siri and Google Now as well as chatbots and automated robotic systems. For now, they are quite simple, but over the next five years they will become much better at making decisions on our behalf in more complex scenarios, which will enable mass adoption of breakthroughs like self-driving cars.