"Over 40 per cent of businesses believe that the model they're using today will cease to exist in five years," says Clare Barclay, chief operating officer of Microsoft UK. "All sorts of technology, AI included, is changing the shape of the business landscape." Data from Microsoft's Maximising the AI Opportunity report shows that early adopters of enterprise AI have already seen a five per cent improvement in productivity, performance and business outcomes compared to those that have yet to explore this exciting new field. The AI tools they're working with include chatbots for first-line customer support and sales, forecasting and data simulation algorithms, and automation functions such as process simulation for science and manufacturing, allowing production lines to be made more efficient. The report combines survey data from 4,000 employees and 1,000 business leaders at enterprises with expert guidance, all to help shed light on the rise of artificial intelligence and help businesses approach AI in an informed, ethical and cost-effective manner. How can technology play a role in helping businesses solve problems?

We introduce a new online learning framework where, at each trial, the learner is required to select a subset of actions from a given known action set. Each action is associated with an energy value, a reward and a cost. The sum of the energies of the actions selected cannot exceed a given energy budget. The goal is to maximise the cumulative profit, where the profit obtained on a single trial is defined as the difference between the maximum reward among the selected actions and the sum of their costs. Action energy values and the budget are known and fixed. All rewards and costs associated with each action change over time and are revealed at each trial only after the learner's selection of actions. Our framework encompasses several online learning problems where the environment changes over time; and the solution trades-off between minimising the costs and maximising the maximum reward of the selected subset of actions, while being constrained to an action energy budget. The algorithm that we propose is efficient and general in that it may be specialised to multiple natural online combinatorial problems.

"Over 40 per cent of businesses believe that the model they're using today will cease to exist in five years," says Clare Barclay, chief operating officer of Microsoft UK. "All sorts of technology, AI included, is changing the shape of the business landscape." Data from Microsoft's Maximising the AI Opportunity report shows that early adopters of enterprise AI have already seen a five per cent improvement in productivity, performance and business outcomes compared to those that have yet to explore this exciting new field. The AI tools they're working with include chatbots for first-line customer support and sales, forecasting and data simulation algorithms, and automation functions such as process simulation for science and manufacturing, allowing production lines to be made more efficient. The report combines survey data from 4,000 employees and 1,000 business leaders at enterprises with expert guidance, all to help shed light on the rise of artificial intelligence and help businesses approach AI in an informed, ethical and cost-effective manner. How can technology play a role in helping businesses solve problems?

Research into techniques that reformulate problems to make general solvers more efficiently derive solutions has attracted much attention, in particular when the reformulation process is to some degree solver and domain independent. There are major challenges to overcome when applying such techniques to automated planning, however: reformulation methods such as adding macro-operators (macros, for short) can be detrimental because they tend to increase branching factors during solution search, while other methods such as learning entanglements can limit a planner's space of potentially solvable problems (its coverage) through over-pruning. These techniques may therefore work well with some domain-problem-planner combinations, but work poorly with others. In this paper we introduce a new learning technique (MUM) for synthesising macros from training example plans in order to improve the speed and coverage of domain independent automated planning engines. MUM embodies domain – independent constraints for selecting macro candidates, for generating macros, and for limiting the size of the grounding set of learned macros, therefore maximising the utility of used macros. Our empirical results with IPC benchmark domains and a range of state of the art planners demonstrate the advance that MUM makes to the increased coverage and efficiency of the planners. Comparisons with a previous leading macro learning mechanism further demonstrate MUM's capability.