Answer Set Programming (ASP) [3] has been shown to be especia lly effective on search and optimization problems whose decision versions are in the class NP, includ ing many problems of practical interest [9, 6]. Despite the ease of modeling and the demonstrated pot ential of ASP, using it poses challenges. In particular, it is unlikely a single solver will emerge tha t would uniformly outperform other solvers. Consequently, selecting a solver for an instance may mean th e difference between solving the problem within an acceptable time and having the solver run "forever ." To address the problem, solver selection, portfolio solving, and automated solver parameter configur ation have all been extensively studied [17, 10, 14, 16, 12].

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.

By incorporating the methods of Answer Set Programming (ASP) and Markov Logic Networks (MLN), LPMLN becomes a powerful tool for non-monotonic, inconsistent and uncertain knowledge representation and reasoning. To facilitate the applications and extend the understandings of LPMLN, we investigate the strong equivalences between LPMLN programs in this paper, which is regarded as an important property in the field of logic programming. In the field of ASP, two programs P and Q are strongly equivalent, iff for any ASP program R, the programs P and Q extended by R have the same stable models. In other words, an ASP program can be replaced by one of its strong equivalent without considering its context, which helps us to simplify logic programs, enhance inference engines, construct human-friendly knowledge bases etc. Since LPMLN is a combination of ASP and MLN, the notions of strong equivalences in LPMLN is quite different from that in ASP. Firstly, we present the notions of p-strong and w-strong equivalences between LPMLN programs. Secondly, we present a characterization of the notions by generalizing the SE-model approach in ASP. Finally, we show the use of strong equivalences in simplifying LPMLN programs, and present a sufficient and necessary syntactic condition that guarantees the strong equivalence between a single LPMLN rule and the empty program.

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.

Since the first conference held in Marseille in 1982, ICLP has been the premier international event for presenting research in logic programming. Contributions are sought in all areas of logic programming, including but not restricted to: Foundations: Semantics, Formalisms, Nonmonotonic reasoning, Knowledge representation. Languages: Concurrency, Objects, Coordination, Mobility, Higher Order, Types, Modes, Assertions, Modules, Meta-programming, Logic-based domain-specific languages, Programming Techniques. Declarative programming: Declarative program development, Analysis, Type and mode inference, Partial evaluation, Abstract interpretation, Transformation, Validation, Verification, Debugging, Profiling, Testing, Execution visualization Implementation: Virtual machines, Compilation, Memory management, Parallel/distributed execution, Constraint handling rules, Tabling, Foreign interfaces, User interfaces. Related Paradigms and Synergies: Inductive and Co-inductive Logic Programming, Constraint Logic Programming, Answer Set Programming, Interaction with SAT, SMT and CSP solvers, Logic programming techniques for type inference and theorem proving, Argumentation, Probabilistic Logic Programming, Relations to object-oriented and Functional programming. Applications: Databases, Big Data, Data integration and federation, Software engineering, Natural language processing, Web and Semantic Web, Agents, Artificial intelligence, Computational life sciences, Education, Cybersecurity, and Robotics.

Human reasoning involves recognising common underlying principles across many examples by utilising variables. The byproducts of such reasoning are invariants that capture patterns across examples such as "if someone went somewhere then they are there" without mentioning specific people or places. Humans learn what variables are and how to use them at a young age, and the question this paper addresses is whether machines can also learn and use variables solely from examples without requiring human pre-engineering. We propose Unification Networks that incorporate soft unification into neural networks to learn variables and by doing so lift examples into invariants that can then be used to solve a given task. We evaluate our approach on four datasets to demonstrate that learning invariants captures patterns in the data and can improve performance over baselines. Humans have the ability to process symbolic knowledge and maintain symbolic thought (Unger & Deacon, 1998). When reasoning, humans do not require combinatorial enumeration of examples but instead utilise invariant patterns with placeholders replacing specific entities. Symbolic cognitive models (Lewis, 1999) embrace this perspective with the human mind seen as an information processing system operating on formal symbols such as reading a stream of tokens in natural language. The language of thought hypothesis (Morton & Fodor, 1978) frames human thought as a structural construct with varying sub-components such as "X went to Y".

Logical rules are a popular knowledge representation language in many domains, representing background knowledge and encoding information that can be derived from given facts in a compact form. However, rule formulation is a complex process that requires deep domain expertise, and is further challenged by today's often large, heterogeneous, and incomplete knowledge graphs. Several approaches for learning rules automatically, given a set of input example facts, have been proposed over time, including, more recently, neural systems. Yet, the area is missing adequate datasets and evaluation approaches: existing datasets often resemble toy examples that neither cover the various kinds of dependencies between rules nor allow for testing scalability. We present a tool for generating different kinds of datasets and for evaluating rule learning systems.

In CoL, computational problems are seen as games between a machine and its environment and logical operators stand for operations on games. It understands interaction among agents in its most general -- game-based -- sense. On the other hand, other formalisms such as situation calculus appear to be too rudimentary to represent complex interactions among agents. In particular, CoL supports query/knowledge duality (or we call it'querying knowledge'): what is a query for one agent becomes new knowledge for another agent. This duality leads to dynamic knowledge migration from one agent to another agent.

Over the last few decades, many distinct lines of research aimed at automating mathematics have been developed, including computer algebra systems (CASs) for mathematical modelling, automated theorem provers for first-order logic, SAT/SMT solvers aimed at program verification, and higher-order proof assistants for checking mathematical proofs. More recently, some of these lines of research have started to converge in complementary ways. One success story is the combination of SAT solvers and CASs (SAT+CAS) aimed at resolving mathematical conjectures. Many conjectures in pure and applied mathematics are not amenable to traditional proof methods. Instead, they are best addressed via computational methods that involve very large combinatorial search spaces. SAT solvers are powerful methods to search through such large combinatorial spaces---consequently, many problems from a variety of mathematical domains have been reduced to SAT in an attempt to resolve them. However, solvers traditionally lack deep repositories of mathematical domain knowledge that can be crucial to pruning such large search spaces. By contrast, CASs are deep repositories of mathematical knowledge but lack efficient general search capabilities. By combining the search power of SAT with the deep mathematical knowledge in CASs we can solve many problems in mathematics that no other known methods seem capable of solving. We demonstrate the success of the SAT+CAS paradigm by highlighting many conjectures that have been disproven, verified, or partially verified using our tool MathCheck. These successes indicate that the paradigm is positioned to become a standard method for solving problems requiring both a significant amount of search and deep mathematical reasoning. For example, the SAT+CAS paradigm has recently been used by Heule, Kauers, and Seidl to find many new algorithms for $3\times3$ matrix multiplication.