I went for tensorflow and installed jupyter for a start and implemented my first models for handwriting recognition. The actual problem is that I have a 15 element input tensor (describing the scheduling scenario) and I want to generate a 4 element output tensor that gives me some parameters for my algorithm. I randomly generated a data set (labled as far as I understand) that contains input and output elements and connects them with a score which should be as low (good) as possible. So if I want to generate a model that gives me as good as possible output so that the algorithm can compute a schedule with the score as good as possible, how can I model a NN that in can be trained by this data later gives me good ouput for any scenario. Output will be used for Java scheduling algorithm.

When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior. Past work on plan explanations primarily involved the AI system explaining the correctness of its plan and the rationale for its decision in terms of its own model. Such soliloquy is wholly inadequate in most realistic scenarios where the humans have domain and task models that differ significantly from that used by the AI system. We posit that the explanations are best studied in light of these differing models. In particular, we show how explanation can be seen as a "model reconciliation problem" (MRP), where the AI system in effect suggests changes to the human's model, so as to make its plan be optimal with respect to that changed human model. We will study the properties of such explanations, present algorithms for automatically computing them, and evaluate the performance of the algorithms.

Many robotic planning applications involve continuous actions with highly non-linear constraints, which cannot be modeled using modern planners that construct a propositional representation. We introduce STRIPStream: an extension of the STRIPS language which can model these domains by supporting the specification of blackbox generators to handle complex constraints. The outputs of these generators interact with actions through possibly infinite streams of objects and static predicates. We provide two algorithms which both reduce STRIPStream problems to a sequence of finite-domain planning problems. The representation and algorithms are entirely domain independent. We demonstrate our framework on simple illustrative domains, and then on a high-dimensional, continuous robotic task and motion planning domain.

Most robots are programmed using one of two methods: learning from demonstration, in which they watch a task being done and then replicate it, or via motion-planning techniques like optimization or sampling, which require a programmer to explicitly specify a task's goals and constraints. Robots that learn from demonstration can't easily transfer one skill they've learned to another situation and remain accurate. On the other hand, motion planning systems that use sampling or optimization can adapt to these changes, but are time-consuming, since they usually have to be hand-coded by expert programmers. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have recently developed a system that aims to bridge the two techniques: C-LEARN, which allows non-coders to teach robots a wider range of tasks simply by providing some information about how objects are typically manipulated and then showing the robot a single demo of the task. Importantly, this enables users to teach robots skills that can be automatically transferred to other robots with different "kinematics" (ways of moving) – a key time- and cost-saving measure for companies that want a range of robots to perform similar actions.

U.S. officials have told airlines to "be prepared" for an expanded ban on carry-on electronic devices allowed on airplanes. Homeland Security spokesman David Lapan confirmed to reporters Tuesday that the administration is considering expanding the ban on laptops, which currently applies to U.S.-bound flights from eight countries in the Middle East and North Africa. An expanded ban on devices larger than cellphones could potentially include "more than a couple" other regions, including flights from Western Europe. Lapan reminded reporters that DHS Secretary John Kelly has alluded to the ban "likely" being expanded. DHS officials, however, are still deciding where and how the new restrictions will be implemented.

We describe a constraint-based automated planner named Transition Constraints for Parallel Planning (TCPP). TCPP constructs its constraint model from a redefined version of the domain transition graphs (DTG) of a given planning problem. TCPP encodes state transitions in the redefined DTGs by using table constraints with cells containing don't cares or wild cards. TCPP uses Minion the constraint solver to solve the constraint model and returns a parallel plan. We empirically compare TCPP with the other state-of-the-art constraint-based parallel planner PaP2. PaP2 encodes action successions in the finite state automata (FSA) as table constraints with cells containing sets of values. PaP2 uses SICStus Prolog as its constraint solver. We also improve PaP2 by using dont cares and mutex constraints. Our experiments on a number of standard classical planning benchmark domains demonstrate TCPP's efficiency over the original PaP2 running on SICStus Prolog and our reconstructed and enhanced versions of PaP2 running on Minion.

Whether for research, purchasing, education or in conjuncation with a family vacation, going to and attending robotics-related conferences and trade shows can provide real personal and business benefits, particularly when the venue is in a foreign location.

FILE - In this Thursday, April 13, 2017, file photo, attorney Thomas Demetrio speaks at a news conference in Chicago. The woman who sobbed after an American Airlines flight attendant took her stroller now has a lawyer, Demetrio, who also represents the Kentucky doctor who was dragged from a United Express flight earlier in the month. American says the woman on the flight on April 21, was supposed to leave her doublewide stroller to be stored in the cargo bay, not take it into the cabin.

Here's a website than can answer that age-old question, "Where's a good place to go?" What it does: It's a no-frills online tool for getting travel inspiration. The front page has three simple settings: What months do you want to travel, how far away from home would you like to go, and are you looking for colder or warmer weather? Recommendations are sent in the form of travel photos from Instagram. What's hot: No need to ask friends for travel suggestions when someone else's travel photos will do just as well. The recommendations tap into the travels of Instagrammers (look for their handle in the top left corner of the photo).

The man dragged off a United Airlines flight in an incident that sparked an international uproar suffered a broken nose and concussion, his lawyer said, adding that he is planning to sue. David Dao has been released from the hospital, attorney Thomas Demetrio said at a news conference on Thursday during which a member of Dao's family spoke out for the first time. Dao's lawyers filed a petition in court requesting that the city, which operates O'Hare International Airport, and United Airlines preserve evidence related to the incident. They also said a lawsuit was forthcoming. "This lawsuit, among other things, hopefully, will create a not just national discussion, but international discussion, on how we're going to be treated going forward," Demetrio said.