We consider the problem of reaching a propositional goal condition in fully-observable nondeterministic (FOND) planning under a general class of fairness assumptions that are given explicitly. The fairness assumptions are of the form A/B and say that state trajectories that contain infinite occurrences of an action a from A in a state s and finite occurrence of actions from B, must also contain infinite occurrences of action a in s followed by each one of its possible outcomes. The infinite trajectories that violate this condition are deemed as unfair, and the solutions are policies for which all the fair trajectories reach a goal state. We show that strong and strong-cyclic FOND planning, as well as QNP planning, a planning model introduced recently for generalized planning, are all special cases of FOND planning with fairness assumptions of this form which can also be combined. FOND+ planning, as this form of planning is called, combines the syntax of FOND planning with some of the versatility of LTL for expressing fairness constraints. A sound and complete FOND+ planner is implemented by reducing FOND+ planning to answer set programs, and its performance is evaluated in comparison with FOND and QNP planners, and LTL synthesis tools. Two other FOND+ planners are introduced as well which are more scalable but are not complete.  

We consider the problem of reaching a propositional goal condition in fully-observable non-deterministic (FOND) planning under a general class of fairness assumptions that are given explicitly. The fairness assumptions are of the form A/B and say that state trajectories that contain infinite occurrences of an action a from A in a state s and finite occurrence of actions from B, must also contain infinite occurrences of action a in s followed by each one of its possible outcomes. The infinite trajectories that violate this condition are deemed as unfair, and the solutions are policies for which all the fair trajectories reach a goal state. We show that strong and strong-cyclic FOND planning, as well as QNP planning, a planning model introduced recently for generalized planning, are all special cases of FOND planning with fairness assumptions of this form which can also be combined. FOND+ planning, as this form of planning is called, combines the syntax of FOND planning with some of the versatility of LTL for expressing fairness constraints. A new planner is implemented by reducing FOND+ planning to answer set programs, and the performance of the planner is evaluated in comparison with FOND and QNP planners, and LTL synthesis tools.

Fincase creates unique IT solutions in the real estate industry that allow you to fully optimize and automate the entire cycle of working with property. The company is using artificial intelligence and machine learning algorithms to transform the real estate appraisal market. The uniqueness of the company is in the creation of an approach to finding special solution for each partner. We do not come with a finished product, but create it together – applying the accumulated experience, and jointly identifying and solving problems. Fincase has been operating in PropTech sector since 2016.

A Quebec-based robotics automation startup has closed a seed round of $6.5 million to further develop and commercialize its artificial intelligence (AI) platform for factory robots. Omnirobotic, founded in 2016 and headquartered in Laval, plans to use the new capital to continue building its autonomous robotic capabilities for production environments. The company intends for its robots to see, plan, and execute processes such as painting, welding, and machining, with limited human oversight. Fonds de solidarité FTQ and Export Development Canada (EDC) led the funding round with participation from Real Ventures and a joint venture including the company's current employees. The Fonds de solidarité FTQ and EDC recently agreed to work closer together to support the growth of companies.

Stradigi AI, a Montréal-based AI solutions provider and research lab founded in 2014, today announced that it has raised $53 million CAD ($40.3 million) in a series A round led by Canadian institutional funds Investissement Québec and Fonds de solidarité FTQ, with participation from Holdun Family Office, Segovia Capital, Cossette, and company cofounders Basil Bouraropoulos and Curtis Gavura. CEO Bouraropoulos said the influx of capital will accelerate Stradigi's North American expansion, which will include new offices in the U.S., with 50 new positions in research, software, sales, and marketing. Additionally, he says it will bolster development of the firm's freshly unveiled AI platform, Kepler, on the heels of a recently announced partnership with professional services network KPMG. "Investissement Québec and the Fonds de solidarité FTQ, in addition to all the other amazing investors that contributed to this financing, are great partners for Stradigi AI," said Bouraropoulos. "As two of the most respected institutional funds in Canada, with diverse portfolios and deep experience with preparing companies for international growth, IQ and the Fonds will bring tremendous value as we execute our strategy to become one of the top three leading platforms in North America." It's built on an adaptable environment that leverages a software-meets-service model, where guidance from Stradigi's research scientists is provided in tandem with solutions deployed via a secure service.

Maharashtra Chief Minister Devendra Fadnavis today said Artificial Intelligence will create more jobs and help in bridging the gap between the rich and the poor. The technology could also help in facilitating the reach of health services to remote areas, he said. Fadnavis was speaking at a panel discussion on Governance and Artificial Intelligence with Vice Premier of Quebec Dominique Anglade in Montreal. Dismissing the fear of losing jobs due to artificial intelligence, he said, "On the contrary, it will create even more jobs and not just that, it will repair and solve many problems. Artificial intelligence has the power to bridge the gap between the rich and the poor." Fadnavis said that due to the information asymmetry in India between the rich and the poor, he proposes to use artificial intelligence tools for access to basic services.

Maharashtra Chief Minister Devendra Fadnavis today said Artificial Intelligence will create more jobs and help in bridging the gap between the rich and the poor. The technology could also help in facilitating the reach of health services to remote areas, he said. Fadnavis was speaking at a panel discussion on Governance and Artificial Intelligence with Vice Premier of Quebec Dominique Anglade in Montreal. Dismissing the fear of losing jobs due to artificial intelligence, he said, "On the contrary, it will create even more jobs and not just that, it will repair and solve many problems. Artificial intelligence has the power to bridge the gap between the rich and the poor." Fadnavis said that due to the information asymmetry in India between the rich and the poor, he proposes to use artificial intelligence tools for access to basic services.