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RedBlack signs deal with artificial intelligence start-up CatsAI

#artificialintelligence

Cybake creator RedBlack Software has signed a partnership agreement with artificial intelligence start-up CatsAI. As of today (21 July), the Cybake bakery management system incorporates CatsAI technology. All Cybake subscribers, which include retail and wholesale bakeries of all types and sizes, now have the option to activate artificially intelligent sales predictions which it said can help increase revenue and reduce waste. The CatsAI technology uses 10,000 data points – including the weather, cultural events and school holidays – to predict how much of each product a bakery should produce on any given day. This is collected from EPoS data and the'brain' does the rest.


The Fast Downward Planning System

Journal of Artificial Intelligence Research

Fast Downward is a classical planning system based on heuristic search. It can deal with general deterministic planning problems encoded in the propositional fragment of PDDL2.2, including advanced features like ADL conditions and effects and derived predicates (axioms). Like other well-known planners such as HSP and FF, Fast Downward is a progression planner, searching the space of world states of a planning task in the forward direction. However, unlike other PDDL planning systems, Fast Downward does not use the propositional PDDL representation of a planning task directly. Instead, the input is first translated into an alternative representation called multi-valued planning tasks, which makes many of the implicit constraints of a propositional planning task explicit. Exploiting this alternative representation, Fast Downward uses hierarchical decompositions of planning tasks for computing its heuristic function, called the causal graph heuristic, which is very different from traditional HSP-like heuristics based on ignoring negative interactions of operators. In this article, we give a full account of Fast Downward's approach to solving multi-valued planning tasks. We extend our earlier discussion of the causal graph heuristic to tasks involving axioms and conditional effects and present some novel techniques for search control that are used within Fast Downward's best-first search algorithm: preferred operators transfer the idea of helpful actions from local search to global best-first search, deferred evaluation of heuristic functions mitigates the negative effect of large branching factors on search performance, and multi-heuristic best-first search combines several heuristic evaluation functions within a single search algorithm in an orthogonal way. We also describe efficient data structures for fast state expansion (successor generators and axiom evaluators) and present a new non-heuristic search algorithm called focused iterative-broadening search, which utilizes the information encoded in causal graphs in a novel way. Fast Downward has proven remarkably successful: It won the "classical'' (i.e., propositional, non-optimising) track of the 4th International Planning Competition at ICAPS 2004, following in the footsteps of planners such as FF and LPG. Our experiments show that it also performs very well on the benchmarks of the earlier planning competitions and provide some insights about the usefulness of the new search enhancements.


The Fast Downward Planning System

Journal of Artificial Intelligence Research

Fast Downward is a classical planning system based on heuristic search. It can deal with general deterministic planning problems encoded in the propositional fragment of PDDL2.2, including advanced features like ADL conditions and effects and derived predicates (axioms). Like other well-known planners such as HSP and FF, Fast Downward is a progression planner, searching the space of world states of a planning task in the forward direction. However, unlike other PDDL planning systems, Fast Downward does not use the propositional PDDL representation of a planning task directly. Instead, the input is first translated into an alternative representation called multi-valued planning tasks, which makes many of the implicit constraints of a propositional planning task explicit.



Classic Paper Award

AI Magazine

David McAllester and David Rosenblitt's paper, "Systematic Nonlinear Planning" (published in the Proceedings of the Ninth National Conference on Artificial Intelligence [AAAI-91]), won the AAAI-10 classic paper award. This commentary by Daniel S. Weld describes the two major impacts the paper had on the field of automated planning. Initially, researchers made many simplifying assumptions, defining the classical planning problem: produce a sequence of atomic actions that will achieve a logically specified goal in a completely known world where action effects are certain. David McAllester and David Rosenblitt's paper, "Systematic Nonlinear Planning" (McAllester and Rosenblitt 1991), presented 19 years ago at the Ninth National Conference on Artificial Intelligence (AAAI-91), had two major impacts on the field: (1) an elegant algorithm and (2) endorsement of the lifting technique. The paper's biggest impact stems from its extremely clear and simple presentation of a sound and complete algorithm (known as SNLP or POP) for classical planning.