This time last year Steve Wozniak was sounding a cautionary note about the future of Artificial Intelligence (AI), warning that computers would one day take over from humans and joking that we might even end up as their pets. In a recent interview with Australia's ABC TV's Lateline the engineering genius appeared more sanguine about the future of self-aware, super-intelligent Artificial Intelligence and much more concerned with the real world killer robots that are all but with us: Lethal Autonomous Weapon Systems (LAWS). The Apple co-founder maintains that human-level Artificial Intelligence won't happen for "a very long time": It might take 200 years before they are really fully able to operate all of their needs in the world, until then they're going to need human beings … I'm not really worried at all. It's very scary to make autonomous weapons that are just following some programmed set of instructions … even when you're driving a car there is no one set of rules … if a lane is closed off you have to do something against the rules … I don't think it's a good idea at all. I don't think we can really stop it.

Which industries will come to the fore in the next decade, and beyond, and become hubs of innovation? According to former State Department official Alec Ross, they won't be the industries that have dominated technology thus far. Instead, artificial intelligence (AI), genomics and robotics will lead the way. On Tuesday, the Italian Embassy in Washington, D.C., held an event to discuss Ross' recently published book, The Industries of the Future. He expounded on the book's themes and highlighted what it will take for individuals, companies and countries to harness the changes that he sees coming to the global economy.

Quietly, Australian postal engineers are building the future. Delivery drones were born first as whimsy and then as marketing, lofty ambitions placed on clumsy, flying toys. We saw them in commercials as gimmicks and on 60 Minutes as a promise of the future. And then, slowly, the flash faded. Amazon's multirotor machine gained a bulky, plane-like body.

Visitors to Australia's federal parliament are often surprised by the robust verbal confrontation between the government and the opposition – technically known as questions without notice, more commonly as question time. A theatrical highpoint of every sitting day, question time is part intellectual cage fight, part kindergarten spat – and all psychological warfare. Political journalists watch the hour-long question time as drought-stricken farmers view the clouds. They look for signs, they read the climate. But what if you were interested in facts?

In the study of argumentation-based reasoning, argument justification has received far more attention than statement justification, often treated as a simple byproduct of the former. As a consequence, counterintuitive results and significant losses of sensitivity can be identified in the treatment of statement justification by otherwise appealing formalisms. To overcome this limitation, we propose to reappraise statement justification as a formalism-independent component. To this purpose, we introduce a novel general model of argumentation-based reasoning based on multiple levels of labellings, one of which is devoted to statement justification. This model is able to encompass several literature proposals as special cases: we illustrate this ability for the case of the ASPIC+ formalism and provide a first example of tunable statement justification in this context.

Most approaches in the field of qualitative spatial reasoning (QSR) use constraint networks to encode spatial scenarios. The size of these networks is quadratic in the number of variables, which has severely limited the real-world application of QSR. In this paper, we propose another representation of spatial scenarios, in which each variable is associated with one or more rectangles. Instead of requiring these rectangles to define a solution of the corresponding constraint network, we construct sequences of rectangles that define partial solutions to progressively weaker constraint networks. We present experimental results that illustrate the effectiveness of this strategy.

A referring expression in linguistics is any noun phrase identifying an object in a way that will be useful to interlocutors. In the context of a query over a first order knowledge base K, constant symbols occurring in K are the artifacts usually used as referring expressions in certain answers to the query. In this paper, we begin to explore how this can be usefully extended by allowing a class of more general formulas, called Singular Referring Expressions, to replace constants in this role. In particular, we lay a foundation for admitting Singular Referring Expressions in certain answer computation for queries over K. An integral part of this foundation are characterization theorems for identification properties of Singular Referring Expressions for queries annotated with a domain specific language for referring concept types. Finally, we apply this framework in the context of tractable description logic dialects, showing how identification properties can be determined at compile-time for conjunctive queries, and how off-the-shelf conjunctive query evaluation for these dialects can be used in query evaluations, preserving, in all cases, underlying tractability.

Consequence-based calculi are a family of reasoning algorithms for description logics (DLs), and they combine hypertableau and resolution in a way that often achieves excellent performance in practice. Up to now, however, they were proposed for either Horn DLs (which do not support disjunction), or for DLs without counting quantifiers. In this paper we present a novel consequence-based calculus for SRIQ — a rich DL that supports both features. This extension is non-trivial since the intermediate consequences that need to be derived during reasoning cannot be captured using DLs themselves. The results of our preliminary performance evaluation suggest the feasibility of our approach in practice.

We study hedonic games with dichotomous preferences. Hedonic games are cooperative games in which players desire to form coalitions, but only care about the makeup of the coalitions of which they are members; they are indifferent about the makeup of other coalitions. The assumption of dichotomous preferences means that, additionally, each player's preference relation partitions the set of coalitions of which that player is a member into just two equivalence classes: satisfactory and unsatisfactory. A player is indifferent between satisfactory coalitions, and is indifferent between unsatisfactory coalitions, but strictly prefers any satisfactory coalition over any unsatisfactory coalition. We develop a succinct representation for such games, in which each player's preference relation is represented by a propositional formula. We show how solution concepts for hedonic games with dichotomous preferences are characterised by propositional formulas.

Many approaches for forgetting in Answer Set Programming (ASP) have been proposed in recent years, in the form of specific operators, or classes of operators, following different principles and obeying different properties. Whereas each approach was developed to somehow address some particular view on forgetting, thus aimed at obeying a specific set of properties deemed adequate for such view, we are lacking a comprehensive and uniform overview of existing operators and properties. We aim at overcoming this by thoroughly examining existing properties and (classes of) operators for forgetting in ASP, drawing a complete picture, which includes many novel (even surprising) results on relations between properties and operators. Our goal is to provide a guide to help users in choosing the most adequate operator for their application requirements.