The rule $\mathrm{Defeated}(x) \leftarrow \mathrm{Attacks}(y,x),\, \neg \, \mathrm{Defeated}(y)$, evaluated under the well-founded semantics (WFS), yields a unique 3-valued (skeptical) solution of an abstract argumentation framework (AF). An argument $x$ is defeated ($\mathrm{OUT}$) if there exists an undefeated argument $y$ that attacks it. For 2-valued (stable) solutions, this is the case iff $y$ is accepted ($\mathrm{IN}$), i.e., if all of $y$'s attackers are defeated. Under WFS, arguments that are neither accepted nor defeated are undecided ($\mathrm{UNDEC}$). As shown in prior work, well-founded solutions (a.k.a. grounded labelings) "explain themselves": The provenance of arguments is given by subgraphs (definable via regular path queries) rooted at the node of interest. This provenance is closely related to winning strategies of a two-player argumentation game. We present a novel approach for extending this provenance to stable AF solutions. Unlike grounded solutions, which can be constructed via a bottom-up alternating fixpoint procedure, stable models often involve non-deterministic choice as part of the search for models. Thus, the provenance of stable solutions is of a different nature, and reflects a more expressive generate & test paradigm. Our approach identifies minimal sets of critical attacks, pinpointing choices and assumptions made by a stable model. These critical attack edges provide additional insights into the provenance of an argument's status, combining well-founded derivation steps with choice steps. Our approach can be understood as a form of diagnosis that finds minimal "repairs" to an AF graph such that the well-founded solution of the repaired graph coincides with the desired stable model of the original AF graph.

Provenance in databases has been thoroughly studied for positive and for recursive queries, then for first-order (FO) queries, i.e., having negation but no recursion. Query evaluation can be understood as a two-player game where the opponents argue whether or not a tuple is in the query answer. This game-theoretic approach yields a natural provenance model for FO queries, unifying how and why-not provenance. Here, we study the fine-grain structure of game provenance. A game $G=(V,E)$ consists of positions $V$ and moves $E$ and can be solved by computing the well-founded model of a single, unstratifiable rule: \[ \text{win}(X) \leftarrow \text{move}(X, Y), \neg \, \text{win}(Y). \] In the solved game $G^{\lambda}$, the value of a position $x\,{\in}\,V$ is either won, lost, or drawn. This value is explained by the provenance $\mathscr{P}$(x), i.e., certain (annotated) edges reachable from $x$. We identify seven edge types that give rise to new kinds of provenance, i.e., potential, actual, and primary, and demonstrate that "not all moves are created equal". We describe the new provenance types, show how they can be computed while solving games, and discuss applications, e.g., for abstract argumentation frameworks.

Fox News senior foreign affairs correspondent Greg Palkot provides details on the August 2021 U.S. drone strike that mistakenly killed 10 civilians. A New York Times report on the investigation into how the U.S. military conducted a drone strike that killed several civilians, including children, in Afghanistan last year, characterized the attack as a "deadly blunder" that was motivated by the "assumptions and biases" of those conducting the strike. The report also claimed that the U.S. military was aware that innocent children had been killed in the attack only hours after the strike, and it made "misleading" statements to the public about that reality. The Times report noted that through a FOIA request, it obtained internal documents from a U.S. Central Command investigation into the August 2021 U.S. drone strike that killed 10 civilians in Kabul, Afghanistan. GENERAL SAYS IT IS UNLIKELY ISIS-K MEMBERS KILLED IN AUGUST KABUL DRONE STRIKE: 'A TRAGIC MISTAKE' Photo taken on Sept. 2, 2021 shows damaged vehicles at the site of the U.S. airstrike in Kabul, capital of Afghanistan.

In addition to being a widely recognised novelist, Milan Kundera has also authored three pieces for theatre: The Owners of the Keys (Majitel\'e kl\'i\v{c}\r{u}, 1961), The Blunder (Pt\'akovina, 1967), and Jacques and his Master (Jakub a jeho p\'an, 1971). In recent years, however, the hypothesis has been raised that Kundera is the true author of a fourth play: Juro J\'ano\v{s}\'ik, first performed in a 1974 production under the name of Karel Steigerwald, who was Kundera's student at the time. In this study, we make use of supervised machine learning to settle the question of authorship attribution in the case of Juro J\'ano\v{s}\'ik, with results strongly supporting the hypothesis of Kundera's authorship.

Monte Carlo Tree Search (MCTS) has produced many recent breakthroughs in game AI research, particularly in computer Go. In this paper we consider how MCTS can be applied to create engaging AI for a popular commercial mobile phone game: Spades by AI Factory, which has been downloaded more than 2.5 million times. In particular, we show how MCTS can be integrated with knowledge-based methods to create an interesting, fun and strong player which makes far fewer plays that could be perceived by human observers as blunders than MCTS without the injection of knowledge. These blunders are particularly noticeable for Spades, where a human player must co-operate with an AI partner. MCTS gives objectively stronger play than the knowledge-based approach used in previous versions of the game and offers the flexibility to customise behaviour whilst maintaining a reusable core, with a reduced development cycle compared to purely knowledge-based techniques.

Artificial Intelligence has been promoted as the Holy Grail of seemingly multitudinous applications for automating decision-making. Some of the more commonplace things AI can improve or quicker than individuals include making film suggestions for Netflix, recognizing diseases, tuning e-commerce and retail sites for every guest, and tweaking in-vehicle infotainment systems. Nonetheless, many times automated frameworks powered by AI have gone wrong. The self-driving car, proposed as a brilliant illustration of what AI can do, bombed when a self-driving Uber SUV murdered a person on foot a year ago. Don't go all surprised with the wonders of AI machines as there are multiple stories of AI experiments gone wrong.

Our recent study using historic data of paddy yield and associated conditions include humidity, luminescence, and temperature. By incorporating regression models and neural networks (NN), one can produce highly satisfactory forecasting of paddy yield. Simulations indicate that our model can predict paddy yield with high accuracy while concurrently detecting diseases that may exist and are oblivious to the human eye. Crop Yield Prediction Using Regression and Neural Networks (CYPUR-NN) is developed here as a system that will facilitate agriculturists and farmers to predict yield from a picture or by entering values via a web interface. CYPUR-NN has been tested on stock images and the experimental results are promising.

Are you likewise the one, considering making yourself something new a thankful when it jumps on your piece of pondering seeking after your energy in 2020. On the off chance that this is so you, at that point we are certain while you are setting aside a few minutes for your own vocation and the innovation information move, you would be taking an interest in, ensure you find a good pace things in the correct manner and in this way, you will be taking assistance of the things to assist you with getting your profession propelled in the correct way. With such a significant number of blasting profession choices in the innovation part, it is so astounding to take note of that things would be acceptable on the off chance that one considers ensuring, the means taken in the correct point of view are constantly more prominent and much better to consider. All things considered, the most buzz making things of 2020 in the innovation area are Artificial Intelligence or the extremely popular AI, Machine learning and the information science advancements. Yet, the most occurring of these three, the AI has been certainly taken into such huge numbers of possibilities while choosing to manufacture a cell phone and continue improving the different sorts of changes they could make in the different stuffs identified with AI and in this way they can think about the things to assist them with making the innovation, considerably additionally conciliating and savvier for the individuals around!

Artificial Intelligence over the years has done wonders in various sectors. And with time this sought-after technology is just getting better and better, making human tasks easier than ever. However, there is a bitter fact and that is it can make mistakes -- after all, it's just technology. As the contribution of AI to humanity has been monumental, its failures have also been equally hilarious. In this article, we are going to take a look at five epic instances when AI has failed to the core.

Reinforcement learning (RL), the category of machine learning that relies on penalties and rewards, can be a powerful technique for teaching machines to adapt to new environments. Deepmind's AlphaGo used it to defeat the world's best Go player despite never having played him before. It has also shown promise in the creation of robots that can perform under changing conditions. But the technique has its limitations. It requires a machine to blunder around as it slowly refines its actions over time.