Setting the price of a product can be a real headache for businesses. It is however a crucial stage which can determine the success or failure of an entire commercial strategy. If the price is too high, customers won't buy the product. Too low, and the obtained margin is too weak to guarantee sufficient revenues. In order to help businesses find the right price, the start-up Brennus Analytics, incubated at ParisTech Entrepreneurs, proposes a software making artificial intelligence technology accessible for businesses.

With Google turning to artificial intelligence to power its flagship search engine business, has the SEO industry been left in the dust? The old ways of testing and measuring are becoming antiquated, and industry insiders are scrambling to understand something new -- something which is more advanced than their backgrounds typically permit. The fact is, even Google engineers are having a hard time explaining how Google works anymore. With this in mind, is artificial intelligence changing the SEO industry for better or worse? And has Google's once-understood algorithm become a "runaway algorithm?"

The cooperative group optimization (CGO) system consists of a group of intelligent agents cooperating with their peers in a sharing environment for realizing a common intention of finding high-quality solution(s) based on the landscape representation of an optimization task. CGO has also been applied on numerical optimization problem (NOP) to find solutions in high-dimensional nonlinear continuous space. Some algorithms, including Dissipative Particle Swarm Optimization (DPSO), Differential Evolution (DE), Social Cognitive Optimization (SCO), Genetic Algorithms (GA), and Electromagnetism-like Mechanism (EM) Heuristic, etc, and their hybrids (e.g., DEPSO), could be easily implemented into CGO. Both SCO and DEPSO have been incorporated into the NLPSolver extension of Calc in Apache Office. DEPSO was used for finding narrow admissible k-tuples.

Near-future electric distribution grids operation will have to rely on demand-side flexibility, both by implementation of demand response strategies and by taking advantage of the intelligent management of increasingly common small-scale energy storage. The Home energy management system (HEMS), installed at low voltage residential clients, will play a crucial role on the flexibility provision to both system operators and market players like aggregators. Modeling and forecasting multi-period flexibility from residential prosumers, such as battery storage and electric water heater, while complying with internal constraints (comfort levels, data privacy) and uncertainty is a complex task. This papers describes a computational method that is capable of efficiently learn and define the feasibility flexibility space from controllable resources connected to a HEMS. An Evolutionary Particle Swarm Optimization (EPSO) algorithm is adopted and reshaped to derive a set of feasible temporal trajectories for the residential net-load, considering storage, flexible appliances, and predefined costumer preferences, as well as load and photovoltaic (PV) forecast uncertainty. A support vector data description (SVDD) algorithm is used to build models capable of classifying feasible and non-feasible HEMS operating trajectories upon request from an optimization/control algorithm operated by a DSO or market player.

We investigate properties of ABA+, a formalism that extends the well studied structured argumentation formalism Assumption-Based Argumentation (ABA) with a preference handling mechanism. In particular, we establish desirable properties that ABA+ semantics exhibit. These pave way to the satisfaction by ABA+ of some (arguably) desirable principles of preference handling in argumentation and nonmonotonic reasoning, as well as non-monotonic inference properties of ABA+ under various semantics.

Team NZ intern Juan Perdomo proved invaluable to the Kiwi America's Cup campaign. Team New Zealand used an artificial intelligence agent to help set up their stunning America's Cup victory. The genius innovations behind the successful Kiwi campaign in Bermuda continue to emerge four months after they blitzed the challenger series and defenders Oracle Team USA to win back the Auld Mug. Stuck in New Zealand and short on money and time to build a second test boat to engage their cycle-powered AC50, the Kiwis went to the computers to find a "virtual" rival to train against. By the time Emirates Team New Zealand lined out against Oracle Team USA for the America's Cup match, helmsman Peter Burling was well schooled in tactics thanks to battling an artificial intelligence agent. The AI was crucial to getting Cup rookie Peter Burling up to speed with starting manoeuvres and tactics in the lightning-fast foiling catamarans.

Artificial intelligence and virtual agents promise improved user experiences and decreased servicing costs. These occur through several pathways. First, virtual agents can be used to ensure the customer is routed to the proper department. Virtual agents are conversational computer programs that interact directly with a customer without human intervention. They are also known as "front end bots", "virtual assistants", or "automated assistants".

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Much research has been done to apply auctions, markets, and negotiation mechanisms to solve the multiagent task allocation problem. However, there has been very little work on human-agent group task allocation. We believe that the notion of bounty hunting has good properties for human-agent group interaction in dynamic task allocation problems. We use previous experimental results comparing bounty hunting with auction-like methods to argue why it would be particularly adept at handling scenarios with unreliable collaborators and unexpectedly hard tasks: scenarios we believe highlight difficulties involved in working with humans collaborators.

In this paper, we demonstrate how a planner (or a robot as an embodiment of it) can explain its decisions to multiple agents in the loop together considering not only the model that it used to come up with its decisions but also the (often misaligned) models of the same task that the other agents might have had. To do this, we build on our previous work on multi-model explanation generation and extend it to account for settings where there is uncertainty of the robot's model of the explainee and/or there are multiple explainees with different models to explain to. We will illustrate these concepts in a demonstration on a robot involved in a typical search and reconnaissance scenario with another human teammate and an external human supervisor.