The Weak Completion Semantics (WCS) is a computational cognitive theory that has shown to be successful in modeling episodes of human reasoning. As the WCS is a recently developed logic programming approach, this paper investigates the correspondence of the WCS with respect to the well-established Answer Set Semantics (ASP). The underlying three-valued logic of both semantics is different and their models are evaluated with respect to different program transformations. We first illustrate these differences by the formal representation of some examples of a well-known psychological experiment, the suppression task. After that, we will provide a translation from logic programs understood under the WCS into logic programs understood under the ASP. In particular, we will show that logic programs under the WCS can be represented as logic programs under the ASP by means of a definition completion, where all defined atoms in a program must be false when their definitions are false.

The first part of the literature review depicts the role AI technology has today as long as some of its risks and negative impacts on society. The table below (Figure 1.4) summarises the themes addressed. This review of the adverse consequences of AI on society only covers the implementation of automated decision systems. It does not encompass any of the issues related to the impact of social media platforms such as filter bubbles, echo chambers of public opinion, data privacy, mass surveillance or discriminatory ads. It also does not provide either any insights regarding the threat of misuse by bad actors or criminals and the danger of a jobless future.

This is an updated version of a story that initially appeared in Interglobix Magazine, the publication for data centers, connectivity and lifestyle. The road to the self-driving car of the future is paved with hardware and data centers. Autonomous vehicles promise to be one of the transformational technologies of the 21st century, with the potential to remake much of our urban and economic landscape. But many questions remain about how the connected car of 2019 will evolve to meet the vision for the autonomous vehicles of the future, and tough issues to be resolved on multiple fronts – including technology, regulation and infrastructure. The long-term vision is to create networks of connected vehicles that "talk" to one another using vehicle-to-vehicle (V2V) communications over low-latency wireless connections, which can also allow vehicle-to-infrastructure (V2I) that enable robot cars to connect with traffic lights and parking meters.

He and his team have played a key part in weaving ML into the very fabric of Workday's underlying platform, which is critical to delivering compelling experiences and outcomes without customers even needing to realize it is there. Earlier in his career, while at a number of Silicon Valley companies, he played a part in making the technology we rely on everyday--GPS, and wifi, for example--so ubiquitous that most of us take these revolutionary technologies for granted. Chakraborty also co-founded and served as chief operating officer at GridCraft, a company that developed simple-to-use data analytics tools that Workday acquired in 2015. Now, as senior vice president of tools and technology at Workday, Chakraborty is responsible for the infrastructure on which our applications are built. In particular, he's leading the charge to make sure that machine learning helps customers make faster, better decisions using all of Workday's products.

After several decades of progress, AI technology is now poised to become a significant source of value for a wide range of businesses. In the 2019 MIT Sloan Management Review and Boston Consulting Group (BCG) Artificial Intelligence Global Executive Study and Research Report, 9 out of 10 respondents agree that AI represents a business opportunity for their company. In addition, a growing number of leaders view AI as not just an opportunity but also a strategic risk: "What if competitors, particularly unencumbered new entrants, figure out AI before we do?" In 2019, 45% perceived some risk from AI, up from an already substantial 37% in 2017. This shift suggests an increasing awareness of and concern with competitors' use of AI.

This paper addresses the problem of multi-agent inverse reinforcement learning (MIRL) in a two-player general-sum stochastic game framework. Five variants of MIRL are considered: uCS-MIRL, advE-MIRL, cooE-MIRL, uCE-MIRL, and uNE-MIRL, each distinguished by its solution concept. Problem uCS-MIRL is a cooperative game in which the agents employ cooperative strategies that aim to maximize the total game value. In problem uCE-MIRL, agents are assumed to follow strategies that constitute a correlated equilibrium while maximizing total game value. Problem uNE-MIRL is similar to uCE-MIRL in total game value maximization, but it is assumed that the agents are playing a Nash equilibrium. Problems advE-MIRL and cooE-MIRL assume agents are playing an adversarial equilibrium and a coordination equilibrium, respectively. We propose novel approaches to address these five problems under the assumption that the game observer either knows or is able to accurately estimate the policies and solution concepts for players. For uCS-MIRL, we first develop a characteristic set of solutions ensuring that the observed bi-policy is a uCS and then apply a Bayesian inverse learning method. For uCE-MIRL, we develop a linear programming problem subject to constraints that define necessary and sufficient conditions for the observed policies to be correlated equilibria. The objective is to choose a solution that not only minimizes the total game value difference between the observed bi-policy and a local uCS, but also maximizes the scale of the solution. We apply a similar treatment to the problem of uNE-MIRL. The remaining two problems can be solved efficiently by taking advantage of solution uniqueness and setting up a convex optimization problem. Results are validated on various benchmark grid-world games.

From its inception, AI has had a rather ambivalent relationship to humans---swinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives at an ever increasing pace, there is a greater need for AI systems to work synergistically with humans. To do this effectively, AI systems must pay more attention to aspects of intelligence that helped humans work with each other---including social intelligence. I will discuss the research challenges in designing such human-aware AI systems, including modeling the mental states of humans in the loop, recognizing their desires and intentions, providing proactive support, exhibiting explicable behavior, giving cogent explanations on demand, and engendering trust. I will survey the progress made so far on these challenges, and highlight some promising directions. I will also touch on the additional ethical quandaries that such systems pose. I will end by arguing that the quest for human-aware AI systems broadens the scope of AI enterprise, necessitates and facilitates true inter-disciplinary collaborations, and can go a long way towards increasing public acceptance of AI technologies.

The Cognitive Computing Market report includes the leading advancements and technological up-gradation that engages the user to inhabit with fine business selections, define their future-based priority growth plans, and to implement the necessary actions. The global Cognitive Computing Market report also offers a detailed summary of key players and their manufacturing procedure with statistical data and profound analysis of the products, contribution, and revenue. Every information given in the report is sourced and verified by our expert team and is collated with precision. To give a broad overview of the current global market trends and strategies led by key businesses, we present the information in a graphical format such as graphs, pie-charts with the superior illustration.

To integrate strategic, tactical and operational decisions, the two-stage optimization has been widely used to guide dynamic decision making. In this paper, we study the two-stage stochastic programming for complex systems with unknown response estimated by simulation. We introduce the global-local metamodel assisted two-stage optimization via simulation that can efficiently employ the simulation resource to iteratively solve for the optimal first- and second-stage decisions. Specifically, at each visited first-stage decision, we develop a local metamodel to simultaneously solve a set of scenario-based second-stage optimization problems, which also allows us to estimate the optimality gap. Then, we construct a global metamodel accounting for the errors induced by: (1) using a finite number of scenarios to approximate the expected future cost occurring in the planning horizon, (2) second-stage optimality gap, and (3) finite visited first-stage decisions. Assisted by the global-local metamodel, we propose a new simulation optimization approach that can efficiently and iteratively search for the optimal first- and second-stage decisions. Our framework can guarantee the convergence of optimal solution for the discrete two-stage optimization with unknown objective, and the empirical study indicates that it achieves substantial efficiency and accuracy.