This article discusses recent trends and concepts in developing new kinds of artificial intelligence (AI) systems which relate to complex facets and different types of human intelligence, especially social, emotional, attentional and ethical intelligence, which to date have been under-discussed. We describe various aspects of multiple human intelligence and learning styles, which may impact on a variety of AI problem domains. Using the concept of multiple intelligence rather than a single type of intelligence, we categorize and provide working definitions of various AI depending on their cognitive skills or capacities. Future AI systems will be able not only to communicate with human actors and each other, but also to efficiently exchange knowledge with abilities of cooperation, collaboration and even co-creating something new and valuable and have meta-learning capacities. Multi-agent systems such as these can be used to solve problems that would be difficult to solve by any individual intelligent agent.

Agent-based modeling (ABM) is a powerful paradigm to gain insight into social phenomena. One area that ABM has rarely been applied is coalition formation. Traditionally, coalition formation is modeled using cooperative game theory. In this paper, a heuristic algorithm is developed that can be embedded into an ABM to allow the agents to find coalition. The resultant coalition structures are comparable to those found by cooperative game theory solution approaches, specifically, the core. A heuristic approach is required due to the computational complexity of finding a cooperative game theory solution which limits its application to about only a score of agents. The ABM paradigm provides a platform in which simple rules and interactions between agents can produce a macro-level effect without the large computational requirements. As such, it can be an effective means for approximating cooperative game solutions for large numbers of agents. Our heuristic algorithm combines agent-based modeling and cooperative game theory to help find agent partitions that are members of a games' core solution. The accuracy of our heuristic algorithm can be determined by comparing its outcomes to the actual core solutions. This comparison achieved by developing an experiment that uses a specific example of a cooperative game called the glove game. The glove game is a type of exchange economy game. Finding the traditional cooperative game theory solutions is computationally intensive for large numbers of players because each possible partition must be compared to each possible coalition to determine the core set; hence our experiment only considers games of up to nine players. The results indicate that our heuristic approach achieves a core solution over 90% of the time for the games considered in our experiment.

Has Covid-19 started to change our attitude to robots and artificial intelligence? Researchers at Heriot-Watt University think so and are working on cutting-edge techniques to ensure a safer world for us all, with the robots doing more of the dirty and dangerous jobs. Professor Helen Hastie, director of the EPSRC-funded Centre for Doctoral Training in Robotics and Autonomous Systems, says: "At Heriot-Watt, we have been working on getting robots to go into hazardous places where people can't or don't want to go, such as offshore or deep underwater. "During the current crisis, a general awareness of the utility of robots has grown, and people can see where robots might be useful in cases when human interventions are now discouraged. This can be in particular'hot-zones' that need to be avoided by people, such as homes of those shielding, and hospitals." One example of Heriot-Watt's ambition is the SPRING project, where robots are designed to reduce stress and loneliness and improve ...

With the rise of online e-commerce platforms, more and more customers prefer to shop online. To sell more products, online platforms introduce various modules to recommend items with different properties such as huge discounts. A web page often consists of different independent modules. The ranking policies of these modules are decided by different teams and optimized individually without cooperation, which might result in competition between modules. Thus, the global policy of the whole page could be sub-optimal. In this paper, we propose a novel multi-agent cooperative reinforcement learning approach with the restriction that different modules cannot communicate. Our contributions are three-fold. Firstly, inspired by a solution concept in game theory named correlated equilibrium, we design a signal network to promote cooperation of all modules by generating signals (vectors) for different modules. Secondly, an entropy-regularized version of the signal network is proposed to coordinate agents' exploration of the optimal global policy. Furthermore, experiments based on real-world e-commerce data demonstrate that our algorithm obtains superior performance over baselines.

Free Coupon Discount - Artificial Intelligence I: Basics and Games in Java, A guide how to create smart applications, AI, genetic algorithms, pruning, heuristics and metaheuristics and Tic Tac Toe Created by Holczer Balazs Students also bought Artificial Intelligence IV - Reinforcement Learning in Java Java Programming Essentials: AP Computer Science A Beginners Eclipse Java IDE Training Course Artificial Intelligence III - Deep Learning in Java Java Swing (GUI) Programming: From Beginner to Expert Preview this Udemy Course GET COUPON CODE Description This course is about the fundamental concepts of artificial intelligence. This topic is getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detecting cancer for example. We may construct algorithms that can have a very good guess about stock price movement in the market. Section 1: path findinf algorithms graph traversal (BFS and DFS) enhanced search algorihtms A* search algorithm Section 2: basic optimization algorithms brute-force search stochastic search and hill climbing algorithm Section 3: heuristics and meta-heuristics tabu search simulated annealing genetic algorithms particle swarm optimization Section 4: minimax algorithm game trees applications of game trees in chess Tic Tac Toe game and its implementation In the first chapter we are going to talk about the basic graph algorithms.

AI Business Directory is a list of key companies (including startups and big corporations) worldwide with products, services, and applications in the fields related to the Artificial Intelligence (AI). A registered user can submit a listing and maintain it for your own business. The listing service is free. Typical AI fields include, but not limited to: Machine Learning (ML), Deep Learning, Cognitive Computing, Natural Language Processing (NLP), Computer Vision, Pattern Recognition, Autonomous Agents and Multi-Agent Systems, Automated Planning and Scheduling, Robotics, Predictive Analytics, etc. Typical AI applications include, but not limited to: Smart Agriculture, Healthcare, Manufacturing, Smart Cities, Smart Grids, Smart Mobility, Smart Lighting, Smart Buildings, Smart Home, Autonomous Vehicles, Supply Chain and Logistics, Cybersecurity, etc.

The guidance of a large swarm is a challenging control problem. Shepherding offers one approach to guide a large swarm using a few shepherding agents (sheepdogs). Noise is an inherent characteristic in many real-world problems. However, the impact of noise on shepherding is not well-studied. This impact could take two forms. First, noise in the sensorial information received by the shepherd about the location of sheep. Second, noise in the ability of the sheepdog to influence sheep due to disturbances caused during actuation. We study both types of noise in this paper. In this paper, we investigate the performance of Str\"{o}mbom\textquoteright s approach under actuation and perception noises. Before studying the effect noise, we needed to ensure that the parameterisation of the algorithm corresponds to a stable performance for the algorithm. This pegged for running a large number of simulations, while increasing the number of random episodes until stability is achieved. We then systematically studies the impact of sensorial and actuation noise on performance. Str\"{o}mbom\textquoteright s approach is found to be more sensitive to actuation noise than perception noise. This implies that it is more important for the shepherding agent to influence the sheep more accurately by reducing actuation noise than attempting to reduce noise in its sensors. Moreover, different levels of noise required different parameterisation for the shepherding agent, where the threshold needed by an agent to decide whether or not to collect astray sheep is different for different noise levels.

In computational In this paper, we present an evolutionary path planning intelligence research, the concept is used more broadly to approach for shepherding that takes into account the collection model and analyze the behaviour of biologically inspired and movement of the swarm (sheep) in addition to the swarms, where multiple agents of different type interact with sheepdog. The problem is different from conventional path each other in a proactive and reactive manner. The reactive planning for robot navigation in the sense that the control agents are analogous to the sheep in the problem; they respond agents (sheepdog) have access to global information when to the presence of the proactive agent, the sheepdog, and are seeking an optimal path, while the movement of others (sheep) repulsed from it. The sheepdog makes a sequence of decisions is purely reactive. The two-phase algorithm starts by identifying to influence the sheep and to guide them towards a goal the path for the sheepdog to move from any initial position area. A recent comprehensive review on the subject can be to a position behind the swarm. The path is constrained to be found in [1]. The shepherding problem using robotic swarms obstacle free and so as not to impact the sheep; lest the sheep is of interest in several applications beyond the biological be repulsed and scatter, making their collection even harder inspiration of shepherding itself; applications include crowd and more time-consuming. In the second phase, the algorithm control [2], cleanup of oil spills [3], disaster relief and rescue plans the path for the sheepdog by identifying the next series operations [4], and security/military procedures [5], among of way points to guide the sheep towards their final destination.

The domain of Embodied AI, in which agents learn to complete tasks through interaction with their environment from egocentric observations, has experienced substantial growth with the advent of deep reinforcement learning and increased interest from the computer vision, NLP, and robotics communities. This growth has been facilitated by the creation of a large number of simulated environments (such as AI2-THOR, Habitat and CARLA), tasks (like point navigation, instruction following, and embodied question answering), and associated leaderboards. While this diversity has been beneficial and organic, it has also fragmented the community: a huge amount of effort is required to do something as simple as taking a model trained in one environment and testing it in another. This discourages good science. We introduce AllenAct, a modular and flexible learning framework designed with a focus on the unique requirements of Embodied AI research. AllenAct provides first-class support for a growing collection of embodied environments, tasks and algorithms, provides reproductions of state-of-the-art models and includes extensive documentation, tutorials, start-up code, and pre-trained models. We hope that our framework makes Embodied AI more accessible and encourages new researchers to join this exciting area. The framework can be accessed at: https://allenact.org/

Action and observation delays exist prevalently in the real-world cyber-physical systems which may pose challenges in reinforcement learning design. It is particularly an arduous task when handling multi-agent systems where the delay of one agent could spread to other agents. To resolve this problem, this paper proposes a novel framework to deal with delays as well as the non-stationary training issue of multi-agent tasks with model-free deep reinforcement learning. We formally define the Delay-Aware Markov Game that incorporates the delays of all agents in the environment. To solve Delay-Aware Markov Games, we apply centralized training and decentralized execution that allows agents to use extra information to ease the non-stationarity issue of the multi-agent systems during training, without the need of a centralized controller during execution. Experiments are conducted in multi-agent particle environments including cooperative communication, cooperative navigation, and competitive experiments. We also test the proposed algorithm in traffic scenarios that require coordination of all autonomous vehicles to show the practical value of delay-awareness. Results show that the proposed delay-aware multi-agent reinforcement learning algorithm greatly alleviates the performance degradation introduced by delay. Codes and demo videos are available at: https://github.com/baimingc/delay-aware-MARL.