In multi-agent path finding (MAPF), it is usually assumed that planning is performed centrally and that the destinations of the agents are common knowledge. We will drop both assumptions and analyze under which conditions it can be guaranteed that the agents reach their respective destinations using implicitly coordinated plans without communication. Furthermore, we will analyze what the computational costs associated with such a coordination regime are. As it turns out, guarantees can be given assuming that the agents are of a certain type. However, the implied computational costs are quite severe. In the distributed setting, we either have to solve a sequence of NP-complete problems or have to tolerate exponentially longer executions. In the setting with destination uncertainty, bounded plan existence becomes PSPACE-complete. This clearly demonstrates the value of communicating about plans before execution starts.

Dynamic Controllability (DC) of a Simple Temporal Problem with Uncertainty (STPU) uses a dynamic decision strategy, rather than a fixed schedule, to tackle temporal uncertainty. We extend this concept to the Controllable Conditional Temporal Problem with Uncertainty (CCTPU), which extends the STPU by conditioning temporal constraints on the assignment of controllable discrete variables. We define dynamic controllability of a CCTPU as the existence of a strategy that decides on both the values of discrete choice variables and the scheduling of controllable time points dynamically. This contrasts with previous work, which made a static assignment of choice variables and dynamic decisions over time points only. We propose an algorithm to find such a fully dynamic strategy. The algorithm computes the "envelope" of outcomes of temporal uncertainty in which a particular assignment of discrete variables is feasible, and aggregates these over all choices. When an aggregated envelope covers all uncertain situations of the CCTPU, the problem is dynamically controllable. However, the algorithm is complete only under certain assumptions. Experiments on an existing set of CCTPU benchmarks show that there are cases in which making both discrete and temporal decisions dynamically it is feasible to satisfy the problem constraints while assigning the discrete variables statically it is not.

Artificial intelligence (AI) is one of the most impactful technological revolutions the world has witnessed. Customers today are increasingly exposed to advanced technologies such as AI-enabled chatbots and intelligent voice assistants like Apple Siri, Google Assistant, and Amazon Alexa, making personalization a high priority for incumbent banks. Today, AI enables financial institutions to solve many critical problems, thereby saving money and increasing the efficiency of the workforce. By deploying AI-based solutions, banks can improve the outcome in various dimensions such as customer service, risk management, cross-sales, etc. A MEDICI research study of 34 major banks across several geographies (US, EU, Singapore, Africa, Australia, and India) found that 27 out of these 34 banks have implemented AI in their front-office functions in the form of a chatbot, virtual assistant, and digital advisor.

Hong Kong (CNN)Boeing Australia on Wednesday announced plans to make a jet drone with artificial intelligence that can act as a "loyal wingman" for manned jet fighters.

A new initiative by the US Army suggests "another significant step towards lethal autonomous weapons," warns a leading artificial-intelligence researcher who has called for a ban on so-called "killer robots." The Army Contracting Command has called on potential vendors in industry and academia to submit ideas to help build its Advanced Targeting and Lethality Automated System (ATLAS), which a Defense Department solicitation says will use artificial intelligence and machine learning to give ground-combat vehicles autonomous targeting capabilities. This will allow weapons to "acquire, identify, and engage targets at least 3X faster than the current manual process," according to the notice. Stuart Russell, a professor of computer science at UC Berkeley and a highly regarded AI expert, tells Quartz he is deeply concerned about the idea of tanks and other land-based fighting vehicles eventually having the capability to fire on their own. "It looks very much as if we are heading into an arms race where the current ban on full lethal autonomy"--a section of US military law that mandates some level of human interaction when actually making the decision to fire--"will be dropped as soon as it's politically convenient to do so," says Russell. The Defense Department contracting officer overseeing the solicitation did not immediately respond to a request for further details on ATLAS.

We present a predictive analysis model for 2019 men's Cricket World Cup. We believe this predictive analysis strategy would be very useful for viewers, sponsors, and team strategists. This would also give insights to various cricket analysts and commentators about the features that play a crucial role in the statistical analysis. This model is developed based on the historical data collected for the 10 participating teams (Afghanistan, Australia, Bangladesh, England, India, New Zealand, Pakistan, South Africa, Sri Lanka, and West Indies). In addition, we test our model on 2015 world cup data and measure the accuracy of predictions.

The impact of cloud on contact center operations continues to grow, even as the industry is well into its second decade of leveraging cloud for everything from supporting home agents to reducing the costs of traditional infrastructure associated with large enterprise deployments. The cloud is now enabling new applications, including those based on Artificial Intelligence (AI), Natural Language Processing (NLP) and Machine Learning (ML). Global market intelligence firm International Data Corporation (IDC) predicts that by 2020, 67% of enterprise infrastructure and software spending will be for cloud-based offerings, including contact center migrations from legacy circuits, switches and expensive on-premise equipment. Moving to the cloud continues to change the game as more and more organizations benefit from a pure OPEX model (with little to no upfront costs required to move to a modern system) and enjoy the natural scale when call volumes increase or decrease seasonally. Securing is a second plus; whereas the world questioned the security of cloud solutions for many years, given new software and platforms being rolled out, cloud contact centers can be even more secure, and in compliance whether with the Payment Card Industry (PCI) Data Security Standard or HIPAA for health applications.

Australian video scale-up Oovvuu has closed a second funding round, raising $4.8 million to drive its global expansion. The company uses proprietary artificial intelligence to read publishers' articles, watch broadcasters' videos and match them together, with the goal of putting relevant news video in every article in the world. Since launching in 2014, the company has partnered with 100 global broadcasters and publishers including The BBC, Reuters, Bloomberg, Agence France Presse, Associated Press and Australia's Seven West Media. Led by Cygnet Capital, the $4.8 million round was heavily oversubscribed and underpinned by institutional investors including Regal Funds Management. It follows Cygnet's initial $3.7 million investment in Oovvuu in early 2018.

Simply put, machine learning (ML) is a process a software application uses to actively learn from imported data, using it in a way humans would use past experiences as a part of their learning process. Business intelligence (BI), on the other hand, is a complex field representing a process that depends on technology to acquire, store, and analyze business-related data. The goal of BI is to reach optimal courses of action in as short time as possible, so the process includes several different aspects, such as analytics, predictive modeling, performance management, data mining, etc. MIT Sloan reports that, according to their survey questioning executives from 168 large companies, two out of five companies have already included ML in their sales and marketing efforts. This information comes as no surprise, as the processes behind machine learning have close ties to those of data mining and predictive modeling. When it comes to processing large amounts of data, there simply is no comparison between what a human and a machine can do, so ML naturally appears on stage as a potent tool BI can greatly benefit from.

Drones could soon help search for murder victims in remote areas. In recent tests, drones equipped with laser scanners identified graves in Australian bushland. Now, the nation's police want to use the technology in an ongoing case. In the investigation, the police suspect that a missing person is buried in a densely forested area. However, all searches so far have come up empty.