Despite recent advances in the field of explainable artificial intelligence systems, a concrete quantitative measure for evaluating the usability of such systems is nonexistent. Ensuring the success of an explanatory interface in interacting with users requires a cyclic, symbiotic relationship between human and artificial intelligence. We, therefore, propose explanatory efficacy, a novel metric for evaluating the strength of the cyclic relationship the interface exhibits. Furthermore, in a user study, we evaluated the perceived affect and workload and recorded the EEG signals of our participants as they interacted with our custom-built, iterative explanatory interface to build personalized recommendation systems. We found that systems for perceptually driven iterative tasks with greater explanatory efficacy are characterized by statistically significant hemispheric differences in neural signals, indicating the feasibility of neural correlates as a measure of explanatory efficacy. These findings are beneficial for researchers who aim to study the circular ecosystem of the human-artificial intelligence partnership. Keywords: Affect; Brain Lateralization; EEG; Explanatory Efficacy; Human-centric Explainable Artificial Intelligence; Interactive Explanation; Workload 1. Introduction Recent advances in artificial intelligence (AI) and machine learning algorithms have resulted in models that not only achieve high predictive performance but also provide explanatory features to support their decisions, increasing model interpretability and transparency in real-world environments [1]. However, merely providing explanations is insufficient. Ultimately, AI should address the problems hindering human-agent interaction. Much of the current work for human-interpretable machine learning systems suffers from a lack of usability and efficacy [2]. Developing such a feedback-based interface for AI systems requires an evaluation on the strength of the cyclic relationship the interface exhibits, which we define as explanatory efficacy . Failing to integrate user knowledge with machine systems can decrease interaction quality to the point of causing interaction breakdowns. Consequently, the systems will lose their ability to justify their recommendations, decisions, or actions, resulting in a loss of trust from their users.

With the increasing growth of social media, people have started relying heavily on the information shared therein to form opinions and make decisions. While such a reliance is motivation for a variety of parties to promote information, it also makes people vulnerable to exploitation by slander, misinformation, terroristic and predatorial advances. In this work, we aim to understand and detect such attempts at persuasion. Existing works on detecting persuasion in text make use of lexical features for detecting persuasive tactics, without taking advantage of the possible structures inherent in the tactics used. We formulate the task as a multi-class classification problem and propose an unsupervised, domain-independent machine learning framework for detecting the type of persuasion used in text, which exploits the inherent sentence structure present in the different persuasion tactics. Our work shows promising results as compared to existing work.

This paper proposes a tentative and original survey of meeting points between Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have been developing quite separately in the last three decades. Some common concerns are identified and discussed such as the types of used representation, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal understanding. Then some methodologies combining reasoning and learning are reviewed (such as inductive logic programming, neuro-symbolic reasoning, formal concept analysis, rule-based representations and ML, uncertainty in ML, or case-based reasoning and analogical reasoning), before discussing examples of synergies between KRR and ML (including topics such as belief functions on regression, EM algorithm versus revision, the semantic description of vector representations, the combination of deep learning with high level inference, knowledge graph completion, declarative frameworks for data mining, or preferences and recommendation). This paper is the first step of a work in progress aiming at a better mutual understanding of research in KRR and ML, and how they could cooperate.

Collaborative robotics requires effective communication between a robot and a human partner. This work proposes a set of interpretive principles for how a robotic arm can use pointing actions to communicate task information to people by extending existing models from the related literature. These principles are evaluated through studies where English-speaking human subjects view animations of simulated robots instructing pick-and-place tasks. The evaluation distinguishes two classes of pointing actions that arise in pick-and- place tasks: referential pointing (identifying objects) and locating pointing (identifying locations). The study indicates that human subjects show greater flexibility in interpreting the intent of referential pointing compared to locating pointing, which needs to be more deliberate. The results also demonstrate the effects of variation in the environment and task context on the interpretation of pointing. Our corpus, experiments and design principles advance models of context, common sense reasoning and communication in embodied communication.

The main goal of this paper is to describe an axiomatic utility theory for Dempster-Shafer belief function lotteries. The axiomatic framework used is analogous to von Neumann-Morgenstern's utility theory for probabilistic lotteries as described by Luce and Raiffa. Unlike the probabilistic case, our axiomatic framework leads to interval-valued utilities, and therefore, to a partial (incomplete) preference order on the set of all belief function lotteries. If the belief function reference lotteries we use are Bayesian belief functions, then our representation theorem coincides with Jaffray's representation theorem for his linear utility theory for belief functions. We illustrate our framework using some examples discussed in the literature, and we propose a simple model based on an interval-valued pessimism index representing a decision-maker's attitude to ambiguity and indeterminacy. Finally, we compare our decision theory with those proposed by Jaffray, Smets, Dubois et al., Giang and Shenoy, and Shafer.

In this paper, we introduce the increasing belief criterion in association rule mining. The criterion uses a recursive application of Bayes' theorem to compute a rule's belief. Extracted rules are required to have their belief increase with their last observation. We extend the taxonomy of association rule mining algorithms with a new branch for Bayesian rule mining~(BRM), which uses increasing belief as the rule selection criterion. In contrast, the well-established frequent association rule mining~(FRM) branch relies on the minimum-support concept to extract rules. We derive properties of the increasing belief criterion, such as the increasing belief boundary, no-prior-worries, and conjunctive premises. Subsequently, we implement a BRM algorithm using the increasing belief criterion, and illustrate its functionality in three experiments: (1)~a proof-of-concept to illustrate BRM properties, (2)~an analysis relating socioeconomic information and chemical exposure data, and (3)~mining behaviour routines in patients undergoing neurological rehabilitation. We illustrate how BRM is capable of extracting rare rules and does not suffer from support dilution. Furthermore, we show that BRM focuses on the individual event generating processes, while FRM focuses on their commonalities. We consider BRM's increasing belief as an alternative criterion to thresholds on rule support, as often applied in FRM, to determine rule usefulness.

Department of Computing Science, Ume a University 90187 Ume a, Sweden Abstract Abstract argumentation has emerged as a method for nonmonotonic reasoning that has gained tremendous traction in the symbolic artificial intelligence community. In the literature, the different approaches to abstract argumentation that were refined over the years are typically evaluated from a logics perspective; an analysis that is based on models of ideal, rational decision-making does not exist. In this paper, we close this gap by analyzing abstract argumentation from the perspective of the rational man paradigm in microeconomic theory. To assess under which conditions abstract argumentation-based choice functions can be considered economically rational, we define a new argumentation principle that ensures compliance with the rational man's reference independence property, which stipulates that a rational agent's preferences over two choice options should not be influenced by the absence or presence of additional options. We show that the argumentation semantics as proposed in Dung's classical paper, as well as all of a range of other semantics we evaluate do not fulfill this newly created principle. Consequently, we investigate how structural properties of argumentation frameworks impact the reference independence principle, and propose a restriction to argumentation expansions that allows all of the evaluated semantics to fulfill the requirements for economically rational argumentation-based choice. For this purpose, we define the rational man's expansion as a normal and noncyclic expansion. Finally, we put reference independence into the context of preference-based argumentation and show that for this argumentation variant, which explicitly model preferences, the rational man's expansion cannot ensure reference independence.

From disrupting elections to targeted ransomware to privacy regulations to deepfakes and malevolent AI, 141 cybersecurity predictions for 2020 did not exhaust the subject so here are additional 42 from senior cybersecurity executives. "2019 saw the cybersecurity industry start to explore AI-based solutions. In the coming months, cybercriminals will start to do the same, integrating AI and machine learning into their malware programs to bypass and infiltrate targeted systems. Current cybersecurity measures rely on'detection and response,' but as attackers begin to leverage AI to bypass existing solutions, companies will be left at a significant disadvantage against these seemingly undetectable campaigns. We could see AI-based malware become prominent in day-to-day attacks"--Guy Caspi, CEO, Deep Instinct "In 2020, we'll see an increasing number of cybercriminals use AI to scale their attacks. AI will open the door to mutating malware based on attackers using genetic algorithms that are ...

AI presents incredible opportunities for organizations to change the way they do business. With 1,000 researchers--including winners of the Turing Award and Fields Medal--in 11 labs, Microsoft has established itself as a leader in AI through its dogged focus on innovation, empowerment, and ethics. Now, the groundbreaking capabilities of AI can move beyond the lab to make a positive impact on every enterprise, every industry. Manufacturers have accrued vast stores of data through digital technologies and IoT--offering new windows into operational efficiency and business performance. Culling that data for actionable insights on predictive maintenance and performance improvements has become increasingly essential for an industry that operates on thin margins.

Released today, The Big Brain Revolution shows the impact of Artificial Intelligence (AI) on the human brain, uncovering hidden secrets from the science of neuro-technology. The author examines the evidence that bombarding 1.4kg of brain matter with pings, dings and rings is re-wiring our neurons - transforming the way humans think and act. AI is able to read signals from your body and brain, detecting when you lie and finding out when you fall in love. The book is a fun fact-filled review of the latest advancements in everything from restoring memory loss to robot parents. Readers will also discover psychological strategies for healthy thinking in a technological age with techniques to help keep control of your own brain.