Diplomats from around the world met in Geneva last week for the United Nations' third Informal Expert Meeting on lethal autonomous weapons systems (LAWS), commonly dubbed "killer robots". Their aim was to make progress on deciding how, or if, LAWS should be regulated under international humanitarian law. A range of views were expressed at the meeting, from Pakistan being in favour of a full ban, to the UK favouring no new regulation for LAWS, and several positions in between. Despite the range of views on offer, there was some common ground. It is generally agreed that LAWS are governed by international humanitarian law.

HEFFNER: I'm Alexander Heffner, your host on The Open Mind. TED Talk curator Chris Anderson joined us recently to consider the danger of artificial intelligence, namely its potential to drive away or make obsolete the moral compass of human beings and civilization as we know it. Of course sometimes, we're our own worst enemy, and we would rather not embrace the present reality. So I've invited today the leading ethicist in the arena of innovation. He's going to help us understand the term techno sapiens as he calls it, with our drones, our supercomputers, our designer babies, and now our 3D printers too. Wendell Wallach is the author of A Dangerous Master: How to Keep Technology From Slipping Beyond Our Control.

We consider the problem of modeling temporal textual data taking endogenous and exogenous processes into account. Such text documents arise in real world applications, including job advertisements and economic news articles, which are influenced by the fluctuations of the general economy. We propose a hierarchical Bayesian topic model which imposes a "group-correlated" hierarchical structure on the evolution of topics over time incorporating both processes, and show that this model can be estimated from Markov chain Monte Carlo sampling methods. We further demonstrate that this model captures the intrinsic relationships between the topic distribution and the time-dependent factors, and compare its performance with latent Dirichlet allocation (LDA) and two other related models. The model is applied to two collections of documents to illustrate its empirical performance: online job advertisements from DirectEmployers Association and journalists' postings on BusinessInsider.com.

With renewable energy becoming more common, energy prices fluctuate more depending on environmental factors such as the weather. Consuming energy without taking volatile prices into consideration can not only become expensive, but may also increase the peak load, which requires energy providers to generate additional energy using less environment-friendly methods. In the Netherlands, pumping stations that maintain the water levels of polder canals are large energy consumers, but the controller software currently used in the industry does not take real-time energy availability into account. We investigate if existing AI planning techniques have the potential to improve upon the current solutions. In particular, we propose a light weight but realistic simulator and investigate if an online planning method (UCT) can utilise this simulator to improve the cost-efficiency of pumping station control policies. An empirical comparison with the current control algorithms indicates that substantial cost, and thus peak load, reduction can be attained.

Reasoning about temporal knowledge is a fundamental task in the area of artificial intelligence and knowledge representation. A key problem in this area is model checking, and indispensable for the state-of-the-art in solving this problem in large-scale settings is the technique of bounded model checking. We investigate the theoretical possibilities of this technique using parameterized complexity theory. In particular, we provide a complete parameterized complexity classification for the model checking problem for symbolically represented Kripke structures for various fragments of the temporal logics LTL, CTL and CTL*. We argue that a known result from the literature for a restricted fragment of LTL can be seen as an fpt-reduction to SAT, and show that such reductions are not possible for any of the other fragments of the temporal logics that we consider. As a by-product of our investigation, we develop a novel parameterized complexity class that can be seen as a parameterized variant of the Polynomial Hierarchy.

We present a novel approach to account for preferences in a well known structured argumentation formalism, Assumption-Based Argumentation (ABA). The new formalism, called ABA+, incorporates object-level preferences (over assumptions) directly into the attack relation to reverse attacks. We give several basic desirable properties of ABA+.

We investigate a higher-order extension of the description logic (DL) SROIQ that provides a fixedly interpreted role semantically coupled with instantiation. It is useful to express interesting meta-level constraints on the modelled ontology. We provide a model-theoretic characterization of the semantics, and we show the decidability by means of reduction.

Methods for learning word representations using large text corpora have received much attention lately due to their impressive performancein numerous natural language processing (NLP) tasks such as, semantic similarity measurement, and word analogy detection.Despite their success, these data-driven word representation learning methods do not considerthe rich semantic relational structure between words in a co-occurring context. On the other hand, already much manual effort has gone into the construction of semantic lexicons such as the WordNetthat represent the meanings of words by defining the various relationships that exist among the words in a language.We consider the question, can we improve the word representations learnt using a corpora by integrating theknowledge from semantic lexicons?. For this purpose, we propose a joint word representation learning method that simultaneously predictsthe co-occurrences of two words in a sentence subject to the relational constrains given by the semantic lexicon.We use relations that exist between words in the lexicon to regularize the word representations learnt from the corpus.Our proposed method statistically significantly outperforms previously proposed methods for incorporating semantic lexicons into wordrepresentations on several benchmark datasets for semantic similarity and word analogy.

In many scenarios where the integration of information into a knowledge base (KB) leads to inconsistencies there is a need to change the KB minimally. In belief revision, relevance postulates meet the minimality requirement by restricting the elimination of KB elements to those that are relevant for the incoming information. This paper focuses on two minimality postulates in an ontology revision scenario in which conflicts are caused by ambiguous use of symbols: a relevance postulate and a generalized inclusion postulate which limits the creativity of the operators. Both postulates exploit the (satisfiably) equivalent representation of a first-order logic KB by its prime implicates, which, intuitively, represent the most atomic logical components of the KB. The paper shows that reinterpretation operators (which are ontology revision operators) fulfill both postulates.

In this paper we show that the problem of deciding the consistency of a knowledge base in the Description Logic ALCM is ExpTime-complete. The M stands for meta-modelling as defined by Motz, Rohrer and Severi. To show our main result, we define an ExpTime Tableau algorithm as an extension of an algorithm for ALC by Nguyen and Szalas.