Every day new travel sites and apps are launched that promise to make trip planning easier. Some do and some don't. Here are two free tools optimized for smartphones that I tested and really liked: Lonely Planet's free Guides app for iOS and Android, and Destinations on Google, which makes it easy to aggregate information for your next travel adventure. The app includes more than 35 free importable guides to international and U.S. destinations, from Bangkok to London and Boston to San Francisco. I tested New York, Kyoto and Vancouver.

We consider the problem of robot path planning in an environment where the location and geometry of obstacles are initially unknown while reusing relevant knowledge about collision avoidance learned from robots’ previous navigational experience. Our main hypothesis in this paper is that the path planning times for a robot can be reduced if it can refer to previous maneuvers it used to avoid collisions with obstacles during earlier missions, and adapt that information to avoid obstacles during its current navigation. To verify this hypothesis,we propose an algorithm called LearnerRRT that first uses a feature matching algorithm called Sample ConsensusInitial Alignment (SAC-IA) to efficiently match currently encountered obstacle features with past obstacle features, and, then uses an experience based learning technique to adapt previously recorded robot obstacle avoidance trajectories corresponding to the matched feature, to the current scenario. The feature matching and machine learning techniques are integrated into the robot’s path planner so that the robot can rapidly and seamlessly update its path to circumvent an obstacle it encounters, in real-time, and continue to move towards its goal. We have conducted several experiments using a simulated Coroware Corobot robot within the Webots simulator to verify the performance of our proposed algorithm,with different start and goal locations, and different obstacle geometries and placements, as well as compared our approach to a state-of-the-art sampling based path planner. Our results show that the proposed algorithm LearnerRRT performs much better than InformedRRT*. When given the same time, our algorithm finished its task successfully whereas Informed RRT* could only achieve 10-20 percent of the optimal distance.

It is nowadays our responsibility to convince our contemporary citizens that AI devices as UGVs (Unmanned Ground Vehicles) and UAVs (Unmanned Aerial Vehicles) are crucial actors of today’s life in a dual domains, both civilian and military. In particular, the decision process is the main component of every military operation and is of high interest because of two main reasons : it is necessary designed to cope with conflict issues and it requires a very complex planning process to be successful. The difficulty to find a good plan is worse in urban areas because of the high uncertainty due to the topology of these areas, the presence of civilians, who can be hostile or friendly, and the unpredictable nature of enemies. The idea in that paper is to qualify what can be a valid computed plan in that context , i.e. welldesigned for recovering of peace, rescue operations after a bombing event, hostage salvage, non-combatant evacuation operations, civil-military co-operation, ...., in urban areas. This planning process leads to associate actually four components, the representation of the tactical scheme, the implementation of the tactical scheme as the behaviour of special forces, military units or emergency squads, the proof process or the explanation process, and finally the handling of external factors depending on the current environment or the current context in which the operation takes place. This paper uses a quaternary representation called the epistemological quadriptych, in order to highlight that the integration of UGVs or UAVs devices requires actually to understand the role of knowledge and behaviour and to provide secure and valid action plans, i.e. which can be explained and justified.

There is a long history in game theory on the topic of Bayesian or "rational" learning, in which each player maintains beliefs over a set of alternative behaviours, or types, for the other players. This idea has gained increasing interest in the artificial intelligence (AI) community, where it is used as a method to control a single agent in a system composed of multiple agents with unknown behaviours. The idea is to hypothesise a set of types, each specifying a possible behaviour for the other agents, and to plan our own actions with respect to those types which we believe are most likely, given the observed actions of the agents. The game theory literature studies this idea primarily in the context of equilibrium attainment. In contrast, many AI applications have a focus on task completion and payoff maximisation. With this perspective in mind, we identify and address a spectrum of questions pertaining to belief and truth in hypothesised types. We formulate three basic ways to incorporate evidence into posterior beliefs and show when the resulting beliefs are correct, and when they may fail to be correct. Moreover, we demonstrate that prior beliefs can have a significant impact on our ability to maximise payoffs in the long-term, and that they can be computed automatically with consistent performance effects. Furthermore, we analyse the conditions under which we are able complete our task optimally, despite inaccuracies in the hypothesised types. Finally, we show how the correctness of hypothesised types can be ascertained during the interaction via an automated statistical analysis.

We study an original problem of pure exploration in a strategic bandit model motivated by Monte Carlo Tree Search. It consists in identifying the best action in a game, when the player may sample random outcomes of sequentially chosen pairs of actions. We propose two strategies for the fixed-confidence setting: Maximin-LUCB, based on lower-and upper-confidence bounds; and Maximin-Racing, which operates by successively eliminating the sub-optimal actions. We discuss the sample complexity of both methods and compare their performance empirically.

Poaching is a serious threat to the conservation of key species and whole ecosystems. While conducting foot patrols is the most commonly used approach in many countries to prevent poaching, such patrols often do not make the best use of limited patrolling resources. To remedy this situation, prior work introduced a novel emerging application called PAWS (Protection Assistant for Wildlife Security); PAWS was proposed as a game-theoretic (``security games'') decision aid to optimize the use of patrolling resources. This paper reports on PAWS's significant evolution from a proposed decision aid to a regularly deployed application, reporting on the lessons from the first tests in Africa in Spring 2014, through its continued evolution since then, to current regular use in Southeast Asia and plans for future worldwide deployment. In this process, we have worked closely with two NGOs (Panthera and Rimba) and incorporated extensive feedback from professional patrolling teams. We outline key technical advances that lead to PAWS's regular deployment: (i) incorporating complex topographic features, e.g., ridgelines, in generating patrol routes; (ii) handling uncertainties in species distribution (game theoretic payoffs); (iii) ensuring scalability for patrolling large-scale conservation areas with fine-grained guidance; and (iv) handling complex patrol scheduling constraints.

We consider the problem of configuration formation in modular robot systems where a set of modules that are initially in different configurations and located at different locations are required to assume appropriate positions so that they can get into a new, user-specified, target configuration. We propose a novel algorithm based on graph isomorphism, where the modules select locations or spots in the target configuration using a utility-based framework, while retaining their original configuration to the greatest extent possible, to reduce the time and energy required by the modules to assume the target configuration. We have shown analytically that our proposed algorithm is complete and guarantees a Pareto-optimal allocation. Experimental simulations of our algorithm with different number of modules in different initial configurations and located initially at different locations, show that the planning time of our algorithm is nominal (order of msec. for 100 modules). We have also compared our algorithm against a market-based allocation algorithm and shown that our proposed algorithm performs better in terms of time and number of messages exchanged.

Active problem solving has been shown to be one of the most effective ways to acquire complex skills. Whether one is learning a programming language by implementing a computer program, or learning calculus by solving problems, context sensitive feedback and guidance are crucial to keeping problem solving efforts fruitful and efficient. This article reviews AI-based algorithms that can diagnose student difficulties during active problem solving and serve as the basis for providing context-sensitive and individualized guidance. The article also describes the crucial role sensor based estimates of cognitive resources such as working memory capacity and attention can play in enhancing the diagnostic capabilities of intelligent instructional systems.

Rodents navigating in a well-known environment can rapidly learn and revisit observed reward locations, often after a single trial. While the mechanism for rapid path planning is unknown, the CA3 region in the hippocampus plays an important role, and emerging evidence suggests that place cell activity during hippocampal preplay periods may trace out future goal-directed trajectories. Here, we show how a particular mapping of space allows for the immediate generation of trajectories between arbitrary start and goal locations in an environment, based only on the mapped representation of the goal. We show that this representation can be implemented in a neural attractor network model, resulting in bump--like activity profiles resembling those of the CA3 region of hippocampus. Neurons tend to locally excite neurons with similar place field centers, while inhibiting other neurons with distant place field centers, such that stable bumps of activity can form at arbitrary locations in the environment. The network is initialized to represent a point in the environment, then weakly stimulated with an input corresponding to an arbitrary goal location. We show that the resulting activity can be interpreted as a gradient ascent on the value function induced by a reward at the goal location. Indeed, in networks with large place fields, we show that the network properties cause the bump to move smoothly from its initial location to the goal, around obstacles or walls. Our results illustrate that an attractor network with hippocampal-like attributes may be important for rapid path planning.

We propose a new integer-linear programming model for the delete relaxation in cost-optimal planning. While a straightforward IP for the delete relaxation is impractical, our enhanced model incorporates variable reduction techniques based on landmarks, relevance-based constraints, dominated action elimination, immediate action application, and inverse action constraints, resulting in an IP that can be used to directly solve delete-free planning problems. We show that our IP model is competitive with previous state-of-the-art solvers for delete-free problems. The LP-relaxation of the IP model is often a very good approximation to the IP, providing an approach to approximating the optimal value of the delete-free task that is complementary to the well-known LM-cut heuristic. We also show that constraints that partially consider delete effects can be added to our IP/LP models. We embed the new IP/LP models into a forward-search based planner, and show that the performance of the resulting planner on standard IPC benchmarks is comparable with the state-of-the-art for cost-optimal planning.