A new challenge for knowledge graph reasoning started in 2018. Deep learning has promoted the application of artificial intelligence (AI) techniques to a wide variety of social problems. Accordingly, being able to explain the reason for an AI decision is b ecoming important to ensure the secure and safe use of AI techniques. Thus, we, the Special Interest Group on Semantic Web and Ontology of the Japanese Society for AI, organized a challenge calling for techniques that reason and/or estimate which character s are criminals while providing a reasonable explanation based on an open knowledge graph of a well - known Sherlock Holmes mystery story . This paper presents a summary report of the first challenge held in 2018, including the knowledge graph construction, t he techniques proposed for reasoning and/or estimation, the evaluation metrics, and the results. The first prize went to an approach that formalized the problem as a constraint satisfaction problem and solved it using a lightweight formal method; the secon d prize went to an approach that used SPARQL and rules; the best resource prize went to a submission that constructed word embedding of characters from all sentences of Sherlock Holmes novels; and the best idea prize went to a discussion multi - agents model . We conclude this paper with the plans and issues for the next challenge in 2019.

Natural language understanding (NLU) of text is a fundamental challenge in AI, and it has received significant attention throughout the history of NLP research. This primary goal has been studied under different tasks, such as Question Answering (QA) and Textual Entailment (TE). In this thesis, we investigate the NLU problem through the QA task and focus on the aspects that make it a challenge for the current state-of-the-art technology. This thesis is organized into three main parts: In the first part, we explore multiple formalisms to improve existing machine comprehension systems. We propose a formulation for abductive reasoning in natural language and show its effectiveness, especially in domains with limited training data. Additionally, to help reasoning systems cope with irrelevant or redundant information, we create a supervised approach to learn and detect the essential terms in questions. In the second part, we propose two new challenge datasets. In particular, we create two datasets of natural language questions where (i) the first one requires reasoning over multiple sentences; (ii) the second one requires temporal common sense reasoning. We hope that the two proposed datasets will motivate the field to address more complex problems. In the final part, we present the first formal framework for multi-step reasoning algorithms, in the presence of a few important properties of language use, such as incompleteness, ambiguity, etc. We apply this framework to prove fundamental limitations for reasoning algorithms. These theoretical results provide extra intuition into the existing empirical evidence in the field.

This volume contains the Regular Papers, Technical Communicationsand the Doctoral Consortium papers of the 35th International Conference on Log ic Programming (ICLP 2019), held in Las Cruces, New Mexico, USA, from September 20-25, 2019. Since the first conference held in Marseille in 1982, ICLP has been the premier international event for presenting research in logic programming. Cont ributions are sought in all areas of logic programming, including but not restricted to: Foundations: Semantics, Formalisms, Nonmonotonic reasoning, Knowledge repre sen-tation.

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.

This paper describes an approach to the methodology of answe r set programming [ Marek and Truszczynski, 1999, Niemel a, 1999] that can facilitate the design of encodings that are easy to u nderstand and provably correct. Under this approach, after appending a rule or a small g roup of rules to the emerging program, the programmer would include a comment that states what has been "achieved" so far, in a certain precise sense. Consider, for instance, the following solution to the 8 quee ns problem, adapted from [ Gebser et al., 2012, Section 3.2 ] .

Emerging technologies have taken the world by storm. The innovations, opportunities, and threats they have unleashed are like no other. Along with their growth, the demand for specialists in these areas has grown. A career in emerging technologies such as machine learning, AI, or data science can be highly lucrative as well as intellectually stimulating. In this article, I have compiled some of the most frequently asked machine learning interview questions with their corresponding answers.

Deduction Theorem: The Problematic Nature of Common Practice in Game Theory Holger I. MEINHARDT † August 2, 2019 We consider the Deduction Theorem that is used in the literature of game theory to run a purported proof by contradiction. In the context of game theory, it is stated that if we have a proof of φ null ϕ, then we also have a proof of φ ϕ. Hence, the proof of φ ϕ is deduced from a previous known statement. However, we argue that one has to manage to prove that the clauses φ and ϕ exist, i.e., they are known true statements in order to establish that φ null ϕ is provable, and that therefore φ ϕ is provable as well. Thus, we are only allowed to reason with known true statements, i.e., we are not allowed to assume that φ or ϕ exist. Doing so, leads immediately to a wrong conclusion. Apart from this, we stress to other facts why the Deduction Theorem is not applicable to run a proof by contradiction. Finally, we present an example from industrial cooperation where the Deduction Theorem is not correctly applied with the consequence that the obtained result contradicts the well-known aggregation issue. MS Classifications 2010: 03B05, 91A12, 91B24 Keywords: Propositional Logic, Deduction Theorem, Herbrand Theorem, Proof by Contradiction, TU Games, Cooperative Oligopoly Games, Partition Function Approach, γ -Belief, Nash Equilibrium, Aggregation across Firms. 1 Introduction We review a common practice in the literature of game theory of applying the Deduction Theorem (Herbrand Theorem, 1930) on a purported proof by contradiction.

A distinctive characteristic of combinatorial problems is their massive search space. This huge domain is due to the number of possible solutions that although finit e, grows exponentially with the amount of data. Some typical combinatorial problems are the search fo r the cheapest or shortest paths, internet data packets routing, protein structure prediction, and planni ng and scheduling of resources. In theory it is possible to find the optimal solution for each c ombinatorial problem by conducting an exhaustive search. However, in practice finding an optimal s olution is often an intractable problem, even for problems of modest size. In this paper, Answer Set Programming (ASP) is used to explor e how to solve the scheduling problem for an Automated Wet-etch Station (A WS) of a Semiconduct or Manufacturing System where the optimization objective is the makespan. If a robot is not use d to transfer jobs between baths, the problem can be approximated as a special case of the most general n o-wait scheduling flowshop problem. A flowshop is a multistage production process where all jobs m ust pass through the same stages. There is a set J of jobs with J N jobs in total.

We investigate the problem of cost-optimal planning in ASP. Current ASP planners can be trivially extended to a cost-optimal one by adding weak constraints, but only for a given makespan (number of steps). It is desirable to have a planner that guarantees global optimality. In this paper, we present two approaches to addressing this problem. First, we show how to engineer a cost-optimal planner composed of two ASP programs running in parallel. Using lessons learned from this, we then develop an entirely new approach to cost-optimal planning, stepless planning, which is completely free of makespan. Experiments to compare the two approaches with the only known cost-optimal planner in SAT reveal good potentials for stepless planning in ASP. The paper is under consideration for acceptance in TPLP.

To be responsive to dynamically changing real-world environments, an intelligent agent needs to perform complex sequential decision-making tasks that are often guided by commonsense knowledge. The previous work on this line of research led to the framework called "interleaved commonsense reasoning and probabilistic planning" (icorpp), which used P-log for representing commmonsense knowledge and Markov Decision Processes (MDPs) or Partially Observable MDPs (POMDPs) for planning under uncertainty. A main limitation of icorpp is that its implementation requires non-trivial engineering efforts to bridge the commonsense reasoning and probabilistic planning formalisms. In this paper, we present a unified framework to integrate icorpp's reasoning and planning components. In particular, we extend probabilistic action language pBC+ to express utility, belief states, and observation as in POMDP models. Inheriting the advantages of action languages, the new action language provides an elaboration tolerant representation of POMDP that reflects commonsense knowledge. The idea led to the design of the system pbcplus2pomdp, which compiles a pBC+ action description into a POMDP model that can be directly processed by off-the-shelf POMDP solvers to compute an optimal policy of the pBC+ action description. Our experiments show that it retains the advantages of icorpp while avoiding the manual efforts in bridging the commonsense reasoner and the probabilistic planner.