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Veteran Democratic strategist James Carville said that he is so frustrated by Republican support for tariffs that he has contemplated punching his computer in rage. Veteran Democratic Party strategist James Carville said that the Republican response to the consequences of tariffs has him contemplating smashing his own computer in rage. CNN host Wolf Blitzer asked Carville what he makes of President Donald Trump's tariffs on goods from Mexico, Canada and China and the fallout. "I've come to think maybe Donald Trump hates the United States," Carville suggested, arguing that Trump's economic and foreign policy strategies are otherwise nonsensical. "I just can't get it out of my mind that I think this man – there's some possibility - we have to consider the possibility that our president hates our country."

Real world applications as in industry and robotics need modelling rich and diverse automated planning problems. Their resolution usually requires coordinated and concurrent action execution. In several cases, these problems are naturally decomposed in a hierarchical way and expressed by a Hierarchical Task Network (HTN) formalism. HDDL, a hierarchical extension of the Planning Domain Definition Language (PDDL), unlike PDDL 2.1 does not allow to represent planning problems with numerical and temporal constraints, which are essential for real world applications. We propose to fill the gap between HDDL and these operational needs and to extend HDDL by taking inspiration from PDDL 2.1 in order to express numerical and temporal expressions. This paper opens discussions on the semantics and the syntax needed for a future HDDL 2.1 extension.

Do It Yourself, but Not Alone: Companion-Technology for Home Improvement – Bringing a Planning-Based Interactive DIY Assistant to Life.

Do It Yourself, but Not Alone: Com- panion-Technology for Home Improvement – Bringing a Planning-Based Interactive DIY Assistant to Life.

Hierarchical Task Network (HTN) planning with Task Insertion (TIHTN planning) is a formalism that hybridizes classical planning with HTN planning by allowing the insertion of operators from outside the method hierarchy. This additional capability has some practical benefits, such as allowing more flexibility for design choices of HTN models: the task hierarchy may be specified only partially, since "missing required tasks" may be inserted during planning rather than prior planning by means of the (predefined) HTN methods. While task insertion in a hierarchical planning setting has already been applied in practice, its theoretical properties have not been studied in detail, yet -- only EXPSPACE membership is known so far. We lower that bound proving NEXPTIME-completeness and further prove tight complexity bounds along two axes: whether variables are allowed in method and action schemas, and whether methods must be totally ordered. We also introduce a new planning technique called acyclic progression, which we use to define provably efficient TIHTN planning algorithms.

Hierarchical Task Network (HTN) planning is a formalism that can express constraints which cannot easily be expressed by classical (non-hierarchical) planning approaches. It enables reasoning about procedural structures and domain-specific search control knowledge. Yet the cornucopia of modern heuristic search techniques remains largely unincorporated in current HTN planners, in part because it is not clear how to estimate the goal distance for a partially-ordered task network. When using SHOP2-style progression, a task network of yet unprocessed tasks is maintained during search. In the general case it can grow arbitrarily large.

The research in hierarchical planning has made considerable progress in the last few years. Many recent systems do not rely on hand-tailored advice anymore to find solutions, but are supposed to be domain-independent systems that come with sophisticated solving techniques. In principle, this development would make the comparison between systems easier (because the domains are not tailored to a single system anymore) and -- much more important -- also the integration into other systems, because the modeling process is less tedious (due to the lack of advice) and there is no (or less) commitment to a certain planning system the model is created for. However, these advantages are destroyed by the lack of a common input language and feature set supported by the different systems. In this paper, we propose an extension to PDDL, the description language used in non-hierarchical planning, to the needs of hierarchical planning systems. We restrict our language to a basic feature set shared by many recent systems, give an extension of PDDL's EBNF syntax definition, and discuss our extensions with respect to several planner-specific input languages from related work.

Heuristics serve as a powerful tool in modern domain-independent planning (DIP) systems by providing critical guidance during the search for high-quality solutions. However, they have not been broadly used with hierarchical planning techniques, which are more expressive and tend to scale better in complex domains by exploiting additional domain-specific knowledge. Complicating matters, we show that for Hierarchical Goal Network (HGN) planning, a goal-based hierarchical planning formalism that we focus on in this paper, any poly-time heuristic that is derived from a delete-relaxation DIP heuristic has to make some relaxation of the hierarchical semantics. To address this, we present a principled framework for incorporating DIP heuristics into HGN planning using a simple relaxation of the HGN semantics we call Hierarchy-Relaxation. This framework allows for computing heuristic estimates of HGN problems using any DIP heuristic in an admissibility-preserving manner. We demonstrate the feasibility of this approach by using the LMCut heuristic to guide an optimal HGN planner. Our empirical results with three benchmark domains demonstrate that simultaneously leveraging hierarchical knowledge and heuristic guidance substantially improves planning performance.

Steven West steps into a remote enemy hideout clad in a 350-pound exoskeleton, sensors piercing the darkness and displaying digital info on his helmet visor, until a shock of static feedback knocks him to the dirty floor. A band of locals surround him with pipes and rebar. "The feedback stopped, leaving his ears ringing, and grainy video feed warped back into view as he was struck again. This scene isn't pulled from the latest Clancy-esque techno-thriller, but a short story written as part of a new Marine Corps exercise using science fiction to think about possible threats 15 to 30 years in the future. "Water's a Fightin' Word" recounts what happens when a squad of Marines on a humanitarian mission in Africa gets surrounded during a global freshwater shortage. The author slips in glimpses of military technology in its infancy today, such as the exoskeleton, electromagnetic pulse weapons, and combat-ready robots, and combines it with likely geopolitical scenarios, such as conflict over water and other environmental resources. Officers at the Marine Corps Warfighting Laboratory/Futures Directorate in Quantico, Va., came up with the idea last year to host a sci-fi contest to spur creativity, as well as get uniformed Marines to conceive of threats in a different way. A total of 84 entries were narrowed down to 18 finalists, who were paired with professional sci-fi writers--including "World War Z's" Max Brooks--during a workshop co-hosted by the Atlantic Council. After months of editing, the top three stories were collected in "Science Fiction Futures: Marine Corps Security Environment Forecast 2030-2045″ and published online [PDF].