Distributed data mining (DDM) deals with the problem of finding patterns or models, called knowledge, in an environment with distributed data and computations. Today, a massive amounts of data which are often geographically distributed and owned by different organisation are being mined. As consequence, a large mount of knowledge are being produced. This causes problems of not only knowledge management but also visualization in data mining. Besides, the main aim of DDM is to exploit fully the benefit of distributed data analysis while minimising the communication. Existing DDM techniques perform partial analysis of local data at individual sites and then generate a global model by aggregating these local results. These two steps are not independent since naive approaches to local analysis may produce an incorrect and ambiguous global data model. The integrating and cooperating of these two steps need an effective knowledge management, concretely an efficient map of knowledge in order to take the advantage of mined knowledge to guide mining the data. In this paper, we present "knowledge map", a representation of knowledge about mined knowledge. This new approach aims to manage efficiently mined knowledge in large scale distributed platform such as Grid. This knowledge map is used to facilitate not only the visualization, evaluation of mining results but also the coordinating of local mining process and existing knowledge to increase the accuracy of final model.

Producing agents that can generalize to a wide range of environments is a significant challenge in reinforcement learning. One method for overcoming this issue is domain randomization, whereby at the start of each training episode some parameters of the environment are randomized so that the agent is exposed to many possible variations. However, domain randomization is highly inefficient and may lead to policies with high variance across domains. In this work, we formalize the domain randomization problem, and show that minimizing the policy's Lipschitz constant with respect to the randomization parameters leads to low variance in the learned policies. We propose a method where the agent only needs to be trained on one variation of the environment, and its learned state representations are regularized during training to minimize this constant. We conduct experiments that demonstrate that our technique leads to more efficient and robust learning than standard domain randomization, while achieving equal generalization scores.

A novel general intelligence model is proposed with three types of learning. A unified sequence of the foreground percept trace and the command trace translates into direct and time-hop observation paths to form the basis of Raw learning. Raw learning includes the formation of image-image associations, which lead to the perception of temporal and spatial relationships among objects and object parts; and the formation of image-audio associations, which serve as the building blocks of language. Offline identification of similar segments in the observation paths and their subsequent reduction into a common segment through merging of memory nodes leads to Generalized learning. Generalization includes the formation of interpolated sensory nodes for robust and generic matching, the formation of sensory properties nodes for specific matching and superimposition, and the formation of group nodes for simpler logic pathways. Online superimposition of memory nodes across multiple predictions, primarily the superimposition of images on the internal projection canvas, gives rise to Innovative learning and thought. The learning of actions happens the same way as raw learning while the action determination happens through the utility model built into the raw learnings, the utility function being the pleasure and pain of the physical senses.

A shield is attached to a system to guarantee safety by correcting the system's behavior at runtime. Existing methods that employ design-time synthesis of shields do not scale to multi-agent systems. Moreover, such shields are typically implemented in a centralized manner, requiring global information on the state of all agents in the system. We address these limitations through a new approach where the shields are synthesized at runtime and do not require global information. There is a shield onboard every agent, which can only modify the behavior of the corresponding agent. In this approach, which is fundamentally decentralized, the shield on every agent has two components: a pathfinder that corrects the behavior of the agent and an ordering mechanism that dynamically modifies the priority of the agent. The current priority determines if the shield uses the pathfinder to modify behavior of the agent. We derive an upper bound on the maximum deviation for any agent from its original behavior. We prove that the worst-case synthesis time is quadratic in the number of agents at runtime as opposed to exponential at design-time for existing methods. We test the performance of the decentralized, runtime shield synthesis approach on a collision-avoidance problem. For 50 agents in a 50x50 grid, the synthesis at runtime requires a few seconds per agent whenever a potential collision is detected. In contrast, the centralized design-time synthesis of shields for a similar setting is intractable beyond 4 agents in a 5x5 grid.

Programming with logic for sophisticated applications must deal with recursion and negation, which have created significant challenges in logic, leading to many different, conflicting semantics of rules. This paper describes a unified language, DA logic, for design and analysis logic, based on the unifying founded semantics and constraint semantics, that support the power and ease of programming with different intended semantics. The key idea is to provide meta constraints, support the use of uncertain information in the form of either undefined values or possible combinations of values, and promote the use of knowledge units that can be instantiated by any new predicates, including predicates with additional arguments.

From a young age humans learn to use grammatical principles to hierarchically combine words into sentences. Action grammars is the parallel idea, that there is an underlying set of rules (a "grammar") that govern how we hierarchically combine actions to form new, more complex actions. We introduce the Action Grammar Reinforcement Learning (AG-RL) framework which leverages the concept of action grammars to consistently improve the sample efficiency of Reinforcement Learning agents. AG-RL works by using a grammar inference algorithm to infer the "action grammar" of an agent midway through training. The agent's action space is then augmented with macro-actions identified by the grammar. We apply this framework to Double Deep Q-Learning (AG-DDQN) and a discrete action version of Soft Actor-Critic (AG-SAC) and find that it improves performance in 8 out of 8 tested Atari games (median +31%, max +668%) and 19 out of 20 tested Atari games (median +96%, maximum +3,756%) respectively without substantive hyperparameter tuning. We also show that AG-SAC beats the model-free state-of-the-art for sample efficiency in 17 out of the 20 tested Atari games (median +62%, maximum +13,140%), again without substantive hyperparameter tuning.

It's a modern day ritual practiced by some of the most passionate fans on the planet: gathering to observe the reveal of new video games. In June, some of the devoted assembled to pay tribute at Nintendo's Rockefeller Center store. Many wore Nintendo t-shirts, hats and other swag. The most hardcore dressed as their favorite characters, including one devotee in full-blown Luigi garb. They were there to watch a livestream of the company's latest "Nintendo Direct," a slickly-produced video announcing upcoming games and more, and get hands-on time with just-announced titles like Link's Awakening, a remake of a 1993 classic.

A Nobel Peace prize winner has warned against robots making life-and-death decision on the battlefield, as it is'unethical and immoral' and can never be undone. Jody Williams made the statement at the United Nations in New York City after the US military announced its project the uses AI to make decisions on what human soldiers should target and destroy. Williams also pointed out the difficulty of holding those involved accountable for certain war crimes, as there will be a programmer, manufacturer, commander and the machine itself involved in the act. Jody Williams (right) has warned against robots making life-and-death decision on the battlefield, as it is'unethical and immoral' and'can never be undone'. She was accompanied with fellow activists Liz O'Sullivan (left) and Mary Wareham (center) Williams won the prestigious accolade in 1997 after leading efforts to ban landmines and is now an advocate with the'Campaign To Stop Killer Robots'.

In The Future of Violence, Benjamin Wittes and Gabriella Blum discuss a disturbing hypothetical scenario. A lone actor in Nigeria, "home to a great deal of spamming and online fraud activity," tricks women and teenage girls into downloading malware that enables him to monitor and record their activity, for the purposes of blackmail. The real story involved a California man who the FBI eventually caught and sent to prison for six years, but if he had been elsewhere in the world he might have gotten away with it. Many countries, as Wittes and Blum note, "have neither the will nor the means to monitor cybercrime, prosecute offenders, or extradite suspects to the United States." Technology is, in other words, enabling criminals to target anyone anywhere and, due to democratization, increasingly at scale.

Cognitive scientist and Dartmouth professor John McCarthy coined the term artificial intelligence (AI) in 1955 when he began his exploration of whether machines could learn and develop formal reasoning like humans. More than 60 years later, AI is the hottest tech topic of the day, from the boardroom to the breakroom. The vast majority of technology executives (91 percent) and 84 percent of the general public believe that AI constitutes the next technology revolution, according to Edelman's 2019 Artificial Intelligence (AI) Survey. PwC has predicted that AI could contribute $15.7 trillion to the global economy by 2030. AI powers voice-based devices, filters our email, and guides our search results.