IMAGE: Researchers collect information for the design of PAWS in a protected area for a trial patrol. A century ago, more than 60,000 tigers roamed the wild. Today, the worldwide estimate has dwindled to around 3,200. Poaching is one of the main drivers of this precipitous drop. Whether killed for skins, medicine or trophy hunting, humans have pushed tigers to near-extinction.

Step by step, rules-based marketing platforms are adding predictive technology and other intelligence on their way to becoming largely self-managing systems. This week, Google Ventures-backed and New York City-based Signpost announced its contribution to that march toward cognitive marketing. Its automated CRM is adding an artificial intelligence agent, dubbed Mia. Signpost's platform already provides a large degree of self-management. It captures data from phone calls, emails, and credit card transactions with a business, and then automatically takes selected marketing actions designed for customer acquisition, customer loyalty, and reviews.

Application of UAV in indoor environment is emerging nowadays due to the advancements in technology. UAV brings more space-flexibility in an occupied or hardly-accessible indoor environment, e.g., shop floor of manufacturing industry, greenhouse, nuclear powerplant. UAV helps in creating an autonomous manufacturing system by executing tasks with less human intervention in time-efficient manner. Consequently, a scheduler is one essential component to be focused on; yet the number of reported studies on UAV scheduling has been minimal. This work proposes a methodology with a heuristic (based on Earliest Available Time algorithm) which assigns tasks to UAVs with an objective of minimizing the makespan. In addition, a quick response towards uncertain events and a quick creation of new high-quality feasible schedule are needed. Hence, the proposed heuristic is incorporated with Particle Swarm Optimization (PSO) algorithm to find a quick near optimal schedule. This proposed methodology is implemented into a scheduler and tested on a few scales of datasets generated based on a real flight demonstration. Performance evaluation of scheduler is discussed in detail and the best solution obtained from a selected set of parameters is reported.

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.

Such a goal can represent either the behaviour model checking systems (e.g., MOCHA (Alur et al. 1998) of the computer system one wants to synthesize and Prism (Kwiatkowska, Norman, and Parker 2011)). Reactive (an automated design problem (Pnueli and Rosner 1989)) Modules supports succinct and high-level modelling or a particular system property which one wants to check of concurrent and multi-agent systems. In the games we (an automated verification problem (Clarke, Grumberg, and study, the preferences of system components are specified Peled 2000)). In this framework, it is assumed that the system by associating with each player in the game a temporal logic plays against an adversarial environment, that is, that (LTL) formula that the player desires to be satisfied. Reactive the goal of the environment is to prevent the system from Modules Games with perfect information (where each player achieving its goal. In game-theoretic terms, this means that can see the entire system state) have been extensively studied the problem is modelled as a zero-sum game, and hence that (Gutierrez, Harrenstein, and Wooldridge 2015a), but in its solution is given by the computation of a winning strategy this paper we focus on imperfect information cases. We study for either the system or the environment.

We introduce EHS*, a novel temporal-epistemic logic defined on temporal intervals characterised by regular expressions. We investigate the complexity of verifying multi-agent systems against EHS* specifications for a number of fragments of EHS* with results ranging from PSPACE-completeness to non-elementary time. The findings show that, at least for the fragments under analysis, the increase in expressiveness obtained by using regular expressions rather than end-points as standard, can be achieved without increasing the complexity of the problem. We show that the expressiveness of regular expressions can also be adopted at the level of specifications without severe computational cost. To do so we introduce a further temporal-epistemic logic, called EHSre, in which regular expressions are used within propositions, and give a polynomial time reduction of the model checking problem from EHSre to EHS*.

There has been a great deal of interest in AI recently in applying Nevertheless, the intuition that underlies sequential and ideas of game theory to model interacting agents who perfect equilibrium, namely, players should play optimally have possibly different preferences as to the outcome of the even off the equilibrium path, seems to make sense even interaction.

We study an epistemic logic where knowledge is built from what the agents observe (including higher-order visibility) and what the agents learn from public announcements. This fixes two main drawbacks of previous observability-based approaches where who sees what is common knowledge and where the epistemic operators distribute over disjunction. The latter forbids the modeling of most of the classical epistemic problems, starting with the muddy children puzzle. We integrate a dynamic dimension where both facts of the world and the agents’ observability can be modified by assignment programs. We establish that the model checking problem is PSPACE-complete.

In temporal logics, the operator F expresses that at some time in the future something happens, e.g., a request is eventually granted. Unfortunately, there is no bound on the time un- til the eventuality is satisfied which in many cases does not correspond to the intuitive meaning system designers have, namely, that F abstracts the idea that there is a bound on this time although its magnitude is not known. An elegant way to capture this meaning is through Prompt-LTL, which extends LTL with the operator F P ("prompt eventually"). We extend this work by studying alternating-time epistemic temporal logics extended with F P . We study the model-checking problem of the logic Prompt- KATL∗, which is ATL∗ extended with epistemic operators and prompt eventually. We also obtain results for the model-checking problem of some of its fragments. Namely, of Prompt-KATL (ATL with epistemic operators and prompt eventually), Prompt-KCTL∗ (CTL∗ with epistemic operators and prompt eventually), and finally the existential fragments of Prompt-KATL∗ and Prompt-KATL.

We review several different languages for collective decision making problems, in which agents express their judgments, opinions, or beliefs over elements of a logically structured domain. Several such languages have been proposed in the literature to compactly represent the questions on which the agents are asked to give their views. In particular, the framework of judgment aggregation allows agents to vote directly on complex, logically related formulas, whereas the setting of binary aggregation asks agents to vote on propositional variables, over which dependencies are expressed by means of an integrity constraint. We compare these two languages and some of their variants according to their relative succinctness and according to the computational complexity of aggregating several individual views expressed in such languages into a collective judgment. Our main finding is that the formula-based language of judgment aggregation is more succinct than the constraint-based language of binary aggregation. In many (but not all) practically relevant situations, this increase in succinctness does not entail an increase in complexity of the corresponding problem of computing the outcome of an aggregation rule.