An important problem of automated planning is validating if a plan complies with the planning domain model. Such validation is straightforward for classical sequential planning but until recently there was no such validation approach for Hierarchical Task Networks (HTN) planning. In this paper we propose a novel technique for validating HTN plans that is based on representing the HTN model as an attribute grammar and using a special parsing algorithm to verify if the plan can be generated by the grammar.

The User Interfaces and Scheduling and Planning (UISP) Workshop had its inaugural meeting at the 2017 International Conference on Automated Scheduling and Planning (ICAPS). The UISP community focuses on bridging the gap between automated planning and scheduling technologies and user interface (UI) technologies. Planning and scheduling systems need UIs, and UIs can be designed and built using planning and scheduling systems. The workshop participants included representatives from government organizations, industry, and academia with various insights and novel challenges. We summarize the discussions from the workshop as well as outline challenges related to this area of research, introducing the now formally established field to the broader user experience and artificial intelligence communities.

One of the major drivers for the progress in scalability of automated planners has been the introduction of the Planning Domain Definition Language (PDDL) and the International Planning Competition (IPC). While PDDL provides a convenient formalism to describe planning problems, there is a significant gap with regards to describing domains. Although PDDL is split into a domain description and a problem description, the domain description is not enough to specify a domain completely, as it does not constrain the possible problems in the domain. For example, there is nothing in the blocksworld PDDL domain description which says that a block can not be on top of itself in the initial state. In this position paper, we argue that PDDL domains should be extended to incorporate a new section which constrains possible problems in the domain. We argue that such an extension can be based on first-order logic, and describe several use cases where this extension might be of use. We also provide some preliminary empirical results of one way for automatically extracting such constraints based on mutual exclusion.

We contribute to recent efforts in relating two approaches to automatic synthesis, namely, automated planning and discrete reactive synthesis. First, we develop a declarative characterization of the standard "fairness" assumption on environments in non-deterministic planning, and show that strong-cyclic plans are correct solution concepts for fair environments. This complements, and arguably completes, the existing foundational work on non-deterministic planning, which focuses on characterizing (and computing) plans enjoying special "structural" properties, namely loopy but closed policy structures. Second, we provide an encoding suitable for reactive synthesis that avoids the naive exponential state space blowup. To do so, special care has to be taken to specify the fairness assumption on the environment in a succinct manner.

Mutex groups are defined in the context of STRIPS planning as sets of facts out of which, maximally, one can be true in any state reachable from the initial state. The importance of computing and exploiting mutex groups was repeatedly pointed out in many studies. However, the theoretical analysis of mutex groups is sparse in current literature. This work provides a complexity analysis showing that inference of mutex groups is as hard as planning itself (PSPACE-Complete) and it also shows a tight relationship between mutex groups and graph cliques. This result motivates us to propose a new type of mutex group called a fact-alternating mutex group (fam-group) of which inference is NP-Complete. Moreover, we introduce an algorithm for the inference of fam-groups based on integer linear programming that is complete with respect to the maximal fam-groups and we demonstrate how beneficial fam-groups can be in the translation of planning tasks into finite domain representation. Finally, we show that fam-groups can be used for the detection of dead-end states and we propose a simple algorithm for the pruning of operators and facts as a preprocessing step that takes advantage of the properties of fam-groups. The experimental evaluation of the pruning algorithm shows a substantial increase in a number of solved tasks in domains from the optimal deterministic track of the last two planning competitions (IPC 2011 and 2014).

GraphGrail Ai heralds the merger of Artificial Intelligence, Blockchain and Big Data into a singularity aimed at assisting businesses and developing the technical and innovative potential of millions of users. As the AI market grows and consumes more branches of various advance industries, the need for sorting, marking up, creating and organizing information into coherent streams of useful data will become a necessary and noble purpose that promises to yield profits for all involved. GraphGrail Ai is the platform that means to unite developers and empower them to create solutions businesses need on the basis of immense amounts of data using blockchain technologies, and monetize on their successful constructs. It is undeniable that Blockchain, big data, and AI are great technologies that are catalyzing the process of innovation and introducing major changes in every industry. Of course, every technology comes with a certain degree of technical complexity and business implications but these innovations have the capacity to redesign the entire technological paradigm from scratch.

For robots to solve real world tasks, they often require the ability to reason about both symbolic and geometric knowledge. We present a framework, called KABouM, for integrating knowledge-level task planning and motion planning in a bounding geometry. By representing symbolic information at the knowledge level, we can model incomplete information, sensing actions and information gain; by representing all geometric entities-- objects, robots and swept volumes of motions--by sets of convex polyhedra, we can efficiently plan manipulation actions and raise reasoning about geometric predicates, such as collisions, to the symbolic level. At the geometric level, we take advantage of our bounded convex decomposition and swept volume computation with quadratic convergence, and fast collision detection of convex bodies. We evaluate our approach on a wide set of problems using real robots, including tasks with multiple manipulators, sensing and branched plans, and mobile manipulation.

A smart FSM solution should bring these pieces together in a seamless way, on a single platform. In this blog, I will examine some of the key capabilities and benefits this integrated approach to FSM enables. A recent Gartner report found that in field service organizations, 40 percent of the workforce is mobile. Businesses still using paper work orders, or those who aren't leveraging mobile technology in the field work are already falling behind. Technicians in the field need a mobile platform that will give them instant access to critical data, even if they lose connectivity.

In multi-objective decision planning and learning, much attention is paid to producing optimal solution sets that contain an optimal policy for every possible user preference profile. We argue that the step that follows, i.e, determining which policy to execute by maximising the user's intrinsic utility function over this (possibly infinite) set, is under-studied. This paper aims to fill this gap. We build on previous work on Gaussian processes and pairwise comparisons for preference modelling, extend it to the multi-objective decision support scenario, and propose new ordered preference elicitation strategies based on ranking and clustering. Our main contribution is an in-depth evaluation of these strategies using computer and human-based experiments. We show that our proposed elicitation strategies outperform the currently used pairwise methods, and found that users prefer ranking most. Our experiments further show that utilising monotonicity information in GPs by using a linear prior mean at the start and virtual comparisons to the nadir and ideal points, increases performance. We demonstrate our decision support framework in a real-world study on traffic regulation, conducted with the city of Amsterdam.

The downhill is Wednesday, so the 22-year-old American suddenly would have had to race on consecutive days. When she tried that earlier at the Pyeongchang Olympics, she followed up her gold in the giant slalom by finishing fourth in the slalom as the defending champion. She pulled out of the super-G on what would have been a third day in a row of racing.