Motivated by the difficulty in presenting computational results, especially when the results are a collection of atoms in a logical language, to users, who are not proficient in computer programming and/or the logical representation of the results, we propose a system for automatic generation of natural language descriptions for applications targeting mainstream users. Differently from many earlier systems with the same aim, the proposed system does not employ templates for the generation task. It assumes that there exist some natural language sentences in the application domain and uses this repository for the natural language description. It does not require, however, a large corpus as it is often required in machine learning approaches. The systems consist of two main components. The first one aims at analyzing the sentences and constructs a Grammatical Framework (GF) for given sentences and is implemented using the Stanford parser and an answer set program. The second component is for sentence construction and relies on GF Library. The paper includes two use cases to demostrate the capability of the system. As the sentence construction is done via GF, the paper includes a use case evaluation showing that the proposed system could also be utilized in addressing a challenge to create an abstract Wikipedia, which is recently discussed in the BlueSky session of the 2018 International Semantic Web Conference.

One of the current problems in decision support from Artifici al Intelligence systems is the lack of explanations. When a system is making decisions in critical co ntexts and those decisions may have an impact on people's life like in the medical or legal domains, then explanations turn to be crucial, especially if we expect that a domain expert relies on the obtaine d answers. One of these situations from the medical domain where explanations have a crucial role is the process of donor-patient matching in an organ transplantation unit. This process starts when a new o rgan is received and consists in selecting a patient among those included in a waiting list for transplan tation. The transplantation unit is expected to follow an objective policy that takes into account medica l parameters and is experimentally supported by the existing records, but more importantly, this decisio n must be easily reproducible and explicable in a comprehensible way for other agents potentially involved, since it may have life-critical consequences at personal, medical and legal levels. Typically, this deci sion is taken in terms of a set of numerical weights (the impact of weights variation is studied in [7]). Although different classification systems based on Artificial Neural Networks (ANNs) are being propose d (see for instance [2] for the case of liver transplantation) their decisions rely on a black box whose b ehaviour is not explicable in human terms. In this paper, we present a rule interpreter, web-liver, designed for assisting the medical experts in the donor-patient matching of a liver transplantation un it, using the case scenario from the Digestive F. Aguado et al.

Barichard & I. St ephan This work is licensed under the Creative Commons Attribution License. Quantified Constraint Handling Rules Vincent Barichard LERIA, University of Angers Angers, France vincent.barichard@univ-angers.fr Igor St ephan LERIA, University of Angers Angers, France igor.stephan@univ-angers.fr We shift the QCSP (Quantified Constraint Satisfaction Problems) framework to the QCHR (Quantified Constraint Handling Rules) framework by enabling dynamic binder and access to user-defined constraints. QCSP offers a natural framework to express PSP ACE problems as finite two-players games. But to define a QCSP model, the binder must be formerly known and cannot be built dynamically even if the worst case won't occur. To overcome this issue, we define the new QCHR formalism that allows to build the binder dynamically during the solving. Our QCHR models exhibit state-of-the-art performances on static binder and outperforms previous QCSP approaches when the binder is dynamic. 1 Introduction We shift the QCSP (for Quantified Constraint Satisfaction Problems) framework to the QCHR (for Quantified Constraint Handling Rule) framework. Such shift is motivated by the difficulties for developing real applications in QCSP . QCSP [7, 26, 15, 4, 22, 20, 3] are a generalization of Constraint Satisfaction Problems (CSP) in which variables may be quantified existentially (as in CSP) and universally. A QCSP is an alternation of existentially and universally quantified variables over finite domains, the binder, followed by a CSP . Universally quantified variables represent uncontrollable parameters such as meteorological events.

Mutual exclusion (mutex) can be traced back to concurrency control, which refers to the condition that prevents simultaneous accesses to a shared resource. In knowledge representation, they specify the constraints that some properties cannot hold at the same time. For example, an object cannot be at different locations at the same time. These constraints frequently occur in applications from model-checking problems in computer-aided verification [2], computer vision [12, 17], graph algorithms [11], and AI planning [14]. The goal of this paper is to develop a graph-theoretic technique for compactly encoding large sets of mutex constraints and apply it to planning in ASP . We do his by focusing on domain-independent AI planning as started out by SA TPlan [10]. That is, we will first obtain an ASP planner by a straightforward translation from SA TPlan and then study how to encode mutex constraints compactly for the planner. In SA T/ASP planning, mutex constraints are specified by formulas/rules that, for any state (which involves a time step, also called a layer in this paper), the actions with conflicting preconditions or effects, and the fluents that are inferred to be conflicting, are mutually exclusive. A naive encoding of these constraints can certainly generate enough rules to overwhelm the underlying solver for large planning instances.

This tool uses an action language ALM to perform inferences on complex interactions of events described in narratives. The methodology used to implement the TEXT2 ALM system was originally outlined by Lierler, Inclezan, and Gelfond [13] via a manual process of converting a narrative to an ALM model. It relies on a conglomeration of resources and techniques from two distinct fields of artificial intelligence, namely, natural language processing and knowledge representation and reasoning. The effectiveness of system TEXT2 ALM is measured by its ability to correctly answer questions from the bAbI tasks published by Facebook Research in 2015. This tool matched or exceeded the performance of state-of-the-art machine learning methods in six of the seven tested tasks. We also illustrate that the TEXT2 ALM approach generalizes to a broader spectrum of narratives. 1 Introduction The field of Information Extraction (IE) is concerned with gathering snippets of meaning from text and storing the derived data in structured, machine interpretable form. Consider a sentence BBDO South in Atlanta, which handles corporate advertising for Georgia-Pacific, will assume additional duties for brands like Angel Soft, said Ken Haldin, a spokesman for Georgia-Pacific from Atlanta. A sample IE system that focuses on identifying organizations and their corporate locations may extract the following predicates from this sentence: locatedIn (BBDOSouth, Atlanta) locatedIn (GeorgiaPaci f ic, Atlanta) These predicates can then be stored either in a relational database or a logic program, and queried accordingly by well-known methods in computer science. Thus, IE allows us to turn unstructured data present in text into structured data easily accessible for automated querying. In this paper, we focus on an IE system that is capable of processing simple narratives with action verbs, in particular, verbs that express physical acts such as go, give, and put. Consider a sample narrative that we refer to as the JS discourse: John traveled to the hallway. We appreciate the insights from Michael Gelfond, Daniela Inclezan, Edward Wertz, and Y uanlin Zhang on their work on language ALM, the C OREALML IB library, and system CALM.

Domain-specific heuristics are an important technique for solving combinatorial problems efficiently. We propose a novel semantics for declarative specifications of domain-specific heuristics in Answer Set Programming (ASP). Decision procedures that are based on a partial solution are a frequent ingredient of existing domain-specific heuristics, e.g., for placing an item that has not been placed yet in bin packing. Therefore, in our novel semantics negation as failure and aggregates in heuristic conditions are evaluated on a partial solver state. State-of-the-art solvers do not allow such a declarative specification. Our implementation in the lazy-grounding ASP system Alpha supports heuristic directives under this semantics. By that, we also provide the first implementation for incorporating declaratively specified domain-specific heuristics in a lazy-grounding setting. Experiments confirm that the combination of ASP solving with lazy grounding and our novel heuristics can be a vital ingredient for solving industrial-size problems.

Recent advances in artificial intelligence have been strongly driven by the use of game environments for training and evaluating agents. Games are often accessible and versatile, with well-defined state-transitions and goals allowing for intensive training and experimentation. However, agents trained in a particular environment are usually tested on the same or slightly varied distributions, and solutions do not necessarily imply any understanding. If we want AI systems that can model and understand their environment, we need environments that explicitly test for this. Inspired by the extensive literature on animal cognition, we present an environment that keeps all the positive elements of standard gaming environments, but is explicitly designed for the testing of animal-like artificial cognition. All source-code is publicly available (see appendix).

WASHINGTON – Several U.S. lawmakers urged caution Tuesday in countering recent attacks on Saudi oil installations, but Trump loyalist Sen. Lindsey Graham branded the incident an "act of war" that merits a decisive response. Graham said it was "clear" that such a sophisticated attack -- drones firing missiles into the world's largest processing plant and an oilfield in Saudi Arabia -- could only have originated with direction and involvement from the "evil regime in Iran." "This is literally an act of war and the goal should be to restore deterrence against Iranian aggression which has clearly been lost," Graham said in a statement. The Republican lawmaker and trusted Trump ally tweeted that Washington should consider an attack on Iran's oil refineries in response, a move that he said "will break the regime's back." Graham has been a defense hawk for years, and he noted that Trump's "measured response" to Iran shooting down an American drone in June "was clearly seen by the Iranian regime as a sign of weakness." A classified briefing book on the attacks was made available to U.S. senators in a secure location in the U.S. Capitol. Other Republican senators, including Marco Rubio and Ron Johnson, said they fully believe Iran was responsible for the Saudi strike.

Google has gone to new heights in order to promote its next-generation Pixel handset. Hanging atop the 49-story Marriott Marquis building in Time Square is an advertisement that gives the first official glimpse at a coral-colored Pixel 4. The promotion also encourages consumer to remind their Google Assistant about its hardware event, set to take place October 15th, where the phone will be unveiled. Hanging atop the Marriott Marquis building in Time Square is an ad that gives the first official glimpse of a coral-colored Pixel 4. The promotion also encourages consumer to remind their Google Assistant about its hardware event, where the phone will be unveiled The hardware event will take place in the Big Apple, where Google will reveal intricate details of its Pixel 4 and Pixel 4 XL – which are set to take on Apple's latest iPhone 11. Rumors have also suggested that Google could announce other devices, including new Google Home Speakers and a Pixelbook 2. Google posted cropped renders of two sleek black devices to its Twitter page. The images appear to show a square module on the back of the phone.

SAN FRANCISCO, September 17, 2019 -- Oracle Exadata Database Machine X8M, available today, sets a new bar and changes the dynamics of the database infrastructure market. Exadata X8M combines Intel Optane DC persistent memory and 100 gigabit remote direct memory access (RDMA) over Converged Ethernet (RoCE) to remove storage bottlenecks and dramatically increase performance for the most demanding workloads such as Online Transaction Processing (OLTP), analytics, IoT, fraud detection, and high frequency trading. "With Exadata X8M, we deliver in-memory performance with all the benefits of shared storage for both OLTP and analytics," said Juan Loaiza, executive vice president, mission-critical database technologies, Oracle. "Reducing response times by an order of magnitude using direct database access to shared persistent memory accelerates every OLTP application, and is a game changer for applications that need real-time access to large amounts of data such as fraud detection and personalized shopping." Exadata X8M helps customers perform existing tasks faster and accelerates time-to-insight, while also enabling deeper and more frequent analyses.