Forecast-Island and Bidding A*-Euclidean Selecting Boustrophedon Coordination Algorithm for Exploration

Exploration algorithms based on the Boustrophedon path seldom consider the impacts of a robot turning at corners on the exploration time. This paper proposes the Forecast-Island and Bidding A*-Euclidean Selecting Boustrophedon Coordination (FIBA*ESBC) algorithm to calculate the turning time at corners in the overall exploration time and introduces a method to estimate the walking time in the Boustrophedon paths in order to determine the directions for path execution. Typically, in bidding-based exploration tasks, the cost is the Euclidean distance between the current position of the robot and the target point. When there is an obstacle between two points, the cost is set to infinity. Therefore, the selected target point is sometimes not optimal. The FIBA*ESBC algorithm is based on the exploration cost of a combination of the Euclidean distance and A* algorithm walking path, which can effectively solve this problem. Because the bidding is based on a greedy algorithm, the robot has a small unexplored island in the later exploration stage; therefore, full exploration is not possible or requires a long time with several repeated paths. The FIBA*ESBC algorithm prioritizes the exploration and estimation of hidden and existing unexplored islands. It can realize complete exploration and decrease the exploration time. Through simulation experiments conducted using Gazebo and RViz, the feasibility of the FIBA*ESBC algorithm is verified. Moreover, a simulation experiment is conducted in MATLAB for comparison with other algorithms. The analysis of the experimental data shows that the proposed algorithm has a relatively short exploration time.

Children’s exploratory play tracks the discriminability of hypotheses

Effective curiosity-driven learning requires recognizing that the value of evidence for testing hypotheses depends on what other hypotheses are under consideration. Do we intuitively represent the discriminability of hypotheses? Here we show children alternative hypotheses for the contents of a box and then shake the box (or allow children to shake it themselves) so they can hear the sound of the contents. We find that children are able to compare the evidence they hear with imagined evidence they do not hear but might have heard under alternative hypotheses. Children (N = 160; mean: 5 years and 4 months) prefer easier discriminations (Experiments 1-3) and explore longer given harder ones (Experiments 4-7). Across 16 contrasts, children’s exploration time quantitatively tracks the discriminability of heard evidence from an unheard alternative. The results are consistent with the idea that children have an “intuitive psychophysics”: children represent their own perceptual abilities and explore longer when hypotheses are harder to distinguish.

ANEGMA: an automated negotiation model for e-markets

We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings.

Touching and hearing the shapes: How auditory angular and curved sounds influence proficiency in recognising tactile angle and curve shapes when experienced and inexperienced in using haptic touch

This study investigated whether adding auditory angular and curved sounds to tactile angle and curve shapes – one unspecified sound to one unspecified shape – positively influences the accuracy and exploration time in recognising tactile angles and curves when experienced and inexperienced in using haptic touch. A within-participant experiment was conducted, with two groups of participants: experienced and inexperienced in using haptic touch, and with two conditions: congruous (e.g., angle shape and angular sound) and incongruous (e.g., angle shape and curved sound) tactile and auditory shape information. Adding congruous auditory angular and curved sounds to tactile angle and curve shapes positively influences the accuracy in recognising tactile angles and curves both when experienced and inexperienced in using haptic touch, and the exploration time on correct recognitions when experienced. People integrate tactile and auditory (angle; curve) shape information and this improves their proficiency in recognising tactile angles and curves.

Optimal Frontier-Based Autonomous Exploration in Unconstructed Environment Using RGB-D Sensor

Aerial robots are widely used in search and rescue applications because of their small size and high maneuvering. However, designing an autonomous exploration algorithm is still a challenging and open task, because of the limited payload and computing resources on board UAVs. This paper presents an autonomous exploration algorithm for the aerial robots that shows several improvements for being used in the search and rescue tasks. First of all, an RGB-D sensor is used to receive information from the environment and the OctoMap divides the environment into obstacles, free and unknown spaces. Then, a clustering algorithm is used to filter the frontiers extracted from the OctoMap, and an information gain based cost function is applied to choose the optimal frontier. At last, the feasible path is given by A* path planner and a safe corridor generation algorithm. The proposed algorithm has been tested and compared with baseline algorithms in three different environments with the map resolutions of 0.2 m, and 0.3 m. The experimental results show that the proposed algorithm has a shorter exploration path and can save more exploration time when compared with the state of the art. The algorithm has also been validated in the real flight experiments.

Relationship between personality and cognitive traits in domestic rabbits (Oryctolagus cuniculus)

Domestication is the process by which species adapt to, and are artificially selected for, human-made environments. Few studies have explored how the process of domestication has affected the connection between behavioral traits and cognitive abilities in animals. This study investigated the relationship between personality and cognitive traits in domestic rabbits (Oryctolagus cuniculus). Fifteen individuals kept in a rabbit rescue facility were tested over a period of two months. We measured the linkage between behavioral traits (response to a novel object and exploration time) and cognitive performance. Our results suggest that there is no relationship between personality traits and problem solving abilities in domestic rabbits. In addition, our results suggest that exploration time is significantly repeatable at the individual level while latency to approach a novel object is not. Thus further research is needed to explore the relationship between cognitive and personality traits in domestic rabbits.

A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation

We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings.

Dynamic Ring Exploration with (H,S) View

The researches about a mobile entity (called agent) on dynamic networks have attracted a lot of attention in recent years. Exploration which requires an agent to visit all the nodes in the network is one of the most fundamental problems. While the exploration of dynamic networks with complete information or with no information about network changes has been studied, an agent with partial information about the network changes has not been considered yet despite its practical importance. In this paper, we consider the exploration of dynamic networks by a single agent with partial information about network changes. To the best of our knowledge, this is the very first work to investigate the exploration problem with such partial information. As a first step in this research direction, we focus on 1-interval connected rings as dynamic networks in this paper. We assume that the single agent has partial information called the ( H , S ) view by which it always knows whether or not each of the links within H hops is available in each of the next S time steps. In this setting, we show that H + S ≥ n and S ≥ ⌈ n / 2 ⌉ (n is the size of the network) are necessary and sufficient conditions to explore 1-interval connected rings. Moreover, we investigate the upper and lower bounds of the exploration time. It is proven that the exploration time is O ( n 2 ) for ⌈ n / 2 ⌉ ≤ S < 2 H ′ − 1 , O ( n 2 / H + n H ) for S ≥ max ( ⌈ n / 2 ⌉ , 2 H ′ − 1 ) , O ( n 2 / H + n log H ) for S ≥ n − 1 , and Ω ( n 2 / H ) for any S where H ′ = min ( H , ⌊ n / 2 ⌋ ) .