AI - Artificial Intelligence AGI - Artificial General Intelligence ANN - Artificial Neural Network ANOVA - Analysis of Variance ANT - Actor Network Theory API - Application Programming Interface APX - Amsterdam Power Exchange AVE - Average Variance Extracted BU - Business Unit CART - Classification and Regression Tree CBMV - Crowd-based Business Model Validation CR - Composite Reliability CT - Computed Tomography CVC - Corporate Venture Capital DR - Design Requirement DP - Design Principle DSR - Design Science Research DSS - Decision Support System EEX - European Energy Exchange FsQCA - Fuzzy-Set Qualitative Comparative Analysis GUI - Graphical User Interface HI-DSS - Hybrid Intelligence Decision Support System HIT - Human Intelligence Task IoT - Internet of Things IS - Information System IT - Information Technology MCC - Matthews Correlation Coefficient ML - Machine Learning OCT - Opportunity Creation Theory OGEMA 2.0 - Open Gateway Energy Management 2.0 OS - Operating System R&D - Research & Development RE - Renewable Energies RQ - Research Question SVM - Support Vector Machine SSD - Solid-State Drive SDK - Software Development Kit TCP/IP - Transmission Control Protocol/Internet Protocol TCT - Transaction Cost Theory UI - User Interface VaR - Value at Risk VC - Venture Capital VPP - Virtual Power Plant Chapter I

Introduction to knowledge representation, expert systems and selected topics including natural language processing, vision and robotics.

Knowledge representation learning has received a lot of attention in the past few years. The success of existing methods heavily relies on the quality of knowledge graphs. The entities with few triplets tend to be learned with less expressive power. Fortunately, there are many knowledge graphs constructed from various sources, the representations of which could contain much information. We propose an adversarial embedding transfer network ATransN, which transfers knowledge from one or more teacher knowledge graphs to a target one through an aligned entity set without explicit data leakage. Specifically, we add soft constraints on aligned entity pairs and neighbours to the existing knowledge representation learning methods. To handle the problem of possible distribution differences between teacher and target knowledge graphs, we introduce an adversarial adaption module. The discriminator of this module evaluates the degree of consistency between the embeddings of an aligned entity pair. The consistency score is then used as the weights of soft constraints. It is not necessary to acquire the relations and triplets in teacher knowledge graphs because we only utilize the entity representations. Knowledge graph completion results show that ATransN achieves better performance against baselines without transfer on three datasets, CN3l, WK3l, and DWY100k. The ablation study demonstrates that ATransN can bring steady and consistent improvement in different settings. The extension of combining other knowledge graph embedding algorithms and the extension with three teacher graphs display the promising generalization of the adversarial transfer network.

In this paper, we introduce an approach to validate the functional equivalence of planning domain models. Validating the functional equivalence of planning domain models is the problem of formally confirming that two planning domain models can be used to solve the same set of problems. The need for techniques to validate the functional equivalence of planning domain models has been highlighted in previous research and has applications in model learning, development and extension. We prove the soundness and completeness of our method. We also develop D-VAL, an automatic functional equivalence validation tool for planning domain models. Empirical evaluation shows that D-VAL validates the functional equivalence of most examined domains in less than five minutes. Additionally, we provide a benchmark to evaluate the feasibility and scalability of this and future related work.

Intelligent agents powered by AI planning assist people in complex scenarios, such as managing teams of semi-autonomous vehicles. However, AI planning models may be incomplete, leading to plans that do not adequately meet the stated objectives, especially in unpredicted situations. Humans, who are apt at identifying and adapting to unusual situations, may be able to assist planning agents in these situations by encoding their knowledge into a planner at run-time. We investigate whether people can collaborate with agents by providing their knowledge to an agent using linear temporal logic (LTL) at run-time without changing the agent's domain model. We presented 24 participants with baseline plans for situations in which a planner had limitations, and asked the participants for workarounds for these limitations. We encoded these workarounds as LTL constraints. Results show that participants' constraints improved the expected return of the plans by 10% ($p < 0.05$) relative to baseline plans, demonstrating that human insight can be used in collaborative planning for resilience. However, participants used more declarative than control constraints over time, but declarative constraints produced plans less similar to the expectation of the participants, which could lead to potential trust issues.

Please join us for a virtual Google workshop on "Conceptual Understanding of Deep Learning" When: May 17th 9am-4pm PST. Goal: How does the Brain/Mind (perhaps even an artificial one) work at an algorithmic level? While deep learning has produced tremendous technological strides in recent decades, there is an unsettling feeling of a lack of "conceptual" understanding of why it works and to what extent it will work in the current form. The goal of the workshop is to bring together theorists and practitioners to develop an understanding of the right algorithmic view of deep learning, characterizing the class of functions that can be learned, coming up with the right learning architecture that may (provably) learn multiple functions, concepts and remember them over time as humans do, theoretical understanding of language, logic, RL, meta learning and lifelong learning. The speakers and panelists include Turing award winners Geoffrey Hinton, Leslie Valiant, and Godel Prize winner Christos Papadimitriou (full-details).

Biz Barrett is an Innovator, Problem Solver, Leader who knows how to fuse Cultural Anthropology, Information Technology and Artificial Intelligence to create successful companies and help a lot of people. With 5G coming, Biz presents his Ideas and System of Companies for the Development of Software and Interactive Media. His Brand and Leadership will not only impact the future of Education, Social Media and Entertainment, but will Solve Problems in the United States and around the World. He seeks funding to evolve His Enterprise 2BZ Information Technologies, Inc. (2BZ IT) into a huge Media Conglomerate. His System of Companies includes 2BZ Media, MyBOTs llc, the Fishnett Plan and GK Tutors, each of which can conduct business on their own but whose resources, function libraries, data and even code can be shared to collaborate on projects.

This paper presents and evaluates two pruning techniques to reinforce the efficiency of constraint optimization solvers based on multi-valued decision-diagrams (MDD). It adopts the branch-and-bound framework proposed by Bergman et al. in 2016 to solve dynamic programs to optimality. In particular, our paper presents and evaluates the effectiveness of the local-bound (LocB) and rough upper-bound pruning (RUB). LocB is a new and effective rule that leverages the approximate MDD structure to avoid the exploration of non-interesting nodes. RUB is a rule to reduce the search space during the development of bounded-width-MDDs. The experimental study we conducted on the Maximum Independent Set Problem (MISP), Maximum Cut Problem (MCP), Maximum 2 Satisfiability (MAX2SAT) and the Traveling Salesman Problem with Time Windows (TSPTW) shows evidence indicating that rough-upper-bound and local-bound pruning have a high impact on optimization solvers based on branch-and-bound with MDDs. In particular, it shows that RUB delivers excellent results but requires some effort when defining the model. Also, it shows that LocB provides a significant improvement automatically; without necessitating any user-supplied information. Finally, it also shows that rough-upper-bound and local-bound pruning are not mutually exclusive, and their combined benefit supersedes the individual benefit of using each technique.

Network spaces have been known as a critical factor in both handcrafted network designs or defining search spaces for Neural Architecture Search (NAS). However, an effective space involves tremendous prior knowledge and/or manual effort, and additional constraints are required to discover efficiency-aware architectures. In this paper, we define a new problem, Network Space Search (NSS), as searching for favorable network spaces instead of a single architecture. We propose an NSS method to directly search for efficient-aware network spaces automatically, reducing the manual effort and immense cost in discovering satisfactory ones. The resultant network spaces, named Elite Spaces, are discovered from Expanded Search Space with minimal human expertise imposed. The Pareto-efficient Elite Spaces are aligned with the Pareto front under various complexity constraints and can be further served as NAS search spaces, benefiting differentiable NAS approaches (e.g. In CIFAR-100, an averagely 2.3% lower error rate and 3.7% closer to target constraint than the baseline with around 90% fewer samples required to find satisfactory networks). Moreover, our NSS approach is capable of searching for superior spaces in future unexplored spaces, revealing great potential in searching for network spaces automatically.

Communication is a cooperative effort that requires reaching mutual understanding among the participants. Humans use commonsense reasoning implicitly to produce natural and logically-coherent responses. As a step towards fluid human-AI communication, we study if response generation (RG) models can emulate human reasoning process and use common sense to help produce better-quality responses. We aim to tackle two research questions: how to formalize conversational common sense and how to examine RG models capability to use common sense? We first propose a task, CEDAR: Causal common sEnse in DiAlogue Response generation, that concretizes common sense as textual explanations for what might lead to the response and evaluates RG models behavior by comparing the modeling loss given a valid explanation with an invalid one. Then we introduce a process that automatically generates such explanations and ask humans to verify them. Finally, we design two probing settings for RG models targeting two reasoning capabilities using verified explanations. We find that RG models have a hard time determining the logical validity of explanations but can identify grammatical naturalness of the explanation easily.