To achieve this, auxiliary information (e.g., texts or attributes) is usually needed

We present a unified approach for constraint displacement problems in which a robot finds a feasible path by displacing constraints or obstacles. To this end, we propose a two stage process that returns locally optimal obstacle displacements to enable a feasible path for the robot. In the second stage, these obstacles are displaced to make the computed robot trajectory feasible, that is, collision-free. Several examples are provided that successfully demonstrate our approach on two distinct classes of constraint displacement problems. Introduction As humans, we encounter various situations in our day to day life in which we alter the location of objects - opening closed doors, repositioning chairs or other movable objects, clear objects while picking an object of interest from a cluttered table-top. As opposed to avoiding each object, altering or displacing these objects or constraints allow us to expand the solution space of feasible paths. In such situations, constraints, such as movable obstacles, may be cleared to find feasible paths. Manipulators often need to rearrange or move obstacles aside to accomplish a given set of tasks - a futuristic robot cooking dinner at home, manipulation in industrial settings, shelves replenishment in a grocery store. Service robots may need to reposition chairs or other movable objects to accomplish a task. A robot may need to plan a path through dynamic obstacles as they might clear the path while moving. We define a constraint displacement problem as one that finds a feasible path by displacing constraints while minimizing a problem-specific objective function.

--In appearance-based localization and mapping, loop closure detection is the process used to determinate if the current observation comes from a previously visited location or a new one. As the size of the internal map increases, so does the time required to compare new observations with all stored locations, eventually limiting online processing. This paper presents an online loop closure detection approach for large-scale and long-term operation. The approach is based on a memory management method, which limits the number of locations used for loop closure detection so that the computation time remains under real-time constraints. The idea consists of keeping the most recent and frequently observed locations in a Working Memory (WM) used for loop closure detection, and transferring the others into a Long-T erm Memory (L TM). When a match is found between the current location and one stored in WM, associated locations stored in L TM can be updated and remembered for additional loop closure detections. Results demonstrate the approach's adaptability and scalability using ten standard data sets from other appearance-based loop closure approaches, one custom data set using real images taken over a 2 km loop of our university campus, and one custom data set (7 hours) using virtual images from the racing video game "Need for Speed: Most Wanted". UTONOMOUS robots operating in real life settings must be able to navigate in large, unstructured, dynamic and unknown spaces. Simultaneous localization and mapping (SLAM) [1] is the capability required by robots to build and update a map of their operating environment and to localize themselves in it. A key feature in SLAM is to recognize previously visited locations. This process is also known as loop closure detection, referring to the fact that coming back to a previously visited location makes it possible to associate this location with another one recently visited. For most of the probabilistic SLAM approaches [2]-[13], loop closure detection is done locally, i.e., matches are found between new observations and a limited region of the map, determined by the uncertainty associated with the robot's Manuscript received April 23, 2012; revised October 2, 2012; accepted January 14, 2013. This paper was recommended for publication by Associate Editor P . This work was supported in part by the Natural Sciences and Engineering Research Council of Canada, the Canadian Foundation for Innovation and the Canada Research Chair program. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Such approaches can be processed under real-time contraints at 30 Hz [14] as long as the estimated position is valid, which cannot be guaranteed in real world situations [15].

Recent work on decentralized computational trust models for open Multi Agent Systems has resulted in the development of CA, a biologically inspired model which focuses on the trustee's perspective. This new model addresses a serious unresolved problem in existing trust and reputation models, namely the inability to handle constantly changing behaviors and agents' continuous entry and exit from the system. In previous work, we compared CA to FIRE, a well-known trust and reputation model, and found that CA is superior when the trustor population changes, whereas FIRE is more resilient to the trustee population changes. Thus, in this paper, we investigate how the trustors can detect the presence of several dynamic factors in their environment and then decide which trust model to employ in order to maximize utility. We frame this problem as a machine learning problem in a partially observable environment, where the presence of several dynamic factors is not known to the trustor and we describe how an adaptable trustor can rely on a few measurable features so as to assess the current state of the environment and then use Deep Q Learning (DQN), in a single-agent Reinforcement Learning setting, to learn how to adapt to a changing environment. We ran a series of simulation experiments to compare the performance of the adaptable trustor with the performance of trustors using only one model (FIRE or CA) and we show that an adaptable agent is indeed capable of learning when to use each model and, thus, perform consistently in dynamic environments.

Location is paramount for businesses that operate physical locations, where it is key to be located close to your target market. This challenge is often the case for franchises that are expanding into new areas where it is important to understand the'fit' of a business in a new area. The aim of this article is to explore this idea in more detail, to evaluate the suitability of a new location for a franchise based on the characteristics of areas where existing franchises are located. To achieve this we will be taking data from OpenStreetMap of a popular coffee shop franchise in Seattle, to use information about the surrounding neighbourhood to identify new prospective locations that are similar. As this task is geospatial, using OpenStreetMap and packages like OSMNX and Geopandas will be useful.

Three police officers were assaulted and six juveniles were arrested after multiple fights broke out at a Mesa skate rink on Saturday night, authorities said. Weekend wrap-up: Here are the biggest Arizona stories from Jan. 3-5 Blowback: Iran abandons nuclear limits after US killing Luxury garage storage company bringing 2 new locations to the Valley Weinstein's reckoning: Trial looms 2 years after #MeToo wave Valley eye surgeon faces multiple charges for alleged billing scheme Weekend wrap-up: Here are the biggest Arizona stories from Jan. 3-5 Counting whales from space pitched as key to saving them Iraq's Parliament calls for expulsion of U.S. troops Tips on how to create, manage your budget in the new year Iraq's Parliament calls for expulsion of U.S. troops Iraq's Parliament calls for expulsion of U.S. troops Arizona ex-fire chief pleads guilty to theft charges A former fire chief accused of embezzling $40,000 from his Arizona district pleaded guilty to felony charges of theft. Valley doctor says soot from candles can be harmful to your health Candles smell good but they're not all that great for your health for one specific reason, according to one Valley doctor. Phoenix lab uses artificial intelligence to slow, manage Alzheimer's disease Arizona is projected to have one of the fastest growing rates of Alzheimer's disease in the country over the next few years, and a clinical lab testing company in the Valley is trying to reverse that. '1917,' 'Once Upon a Time ...in Hollywood' win Golden Globes See winners from the 2020 Golden Globes, hosted by Ricky Gervais.

We can also see this visually. We can verify the convergence of the chains formally using the Gelman Rubin test. Values close to 1.0 mean convergence. We can also test for correlation between samples in the chains. We are aiming for zero auto-correlation to get "random" samples from the posterior distribution.

Interactive Fiction (IF) games are complex textual decision making problems. This paper introduces NAIL, an autonomous agent for general parser-based IF games. NAIL won the 2018 Text Adventure AI Competition, where it was evaluated on twenty unseen games. This paper describes the architecture, development, and insights underpinning NAIL's performance.

The conventional wisdom is that being in the right location is critical to success in retail. As one Japanese convenience store pursues an expansion, it may be getting some non-human help to decide where its stores should go. Convenience store chain Lawson is considering using artificial intelligence (AI) to determine where to place its new store locations, according to the Japan Times. The chain plans to use AI to collect marketing data, such as household distribution patterns and traffic volume, to determine a given store's chances of success in an area. Generally, the chain makes such decisions based on information gathering and analysis of an area carried out by staff.