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We introduce QuaterGCN, a spectral Graph Convolutional Network (GCN) with quaternion-valued weights at whose core lies the Quaternionic Laplacian, a quaternion-valued Laplacian matrix by whose proposal we generalize two widely-used Laplacian matrices: the classical Laplacian (defined for undirected graphs) and the complex-valued Sign-Magnetic Laplacian (proposed to handle digraphs with weights of arbitrary sign). In addition to its generality, our Quaternionic Laplacian is the only Laplacian to completely preserve the topology of a digraph, as it can handle graphs and digraphs containing antiparallel pairs of edges (digons) of different weights without reducing them to a single (directed or undirected) edge as done with other Laplacians. Experimental results show the superior performance of QuaterGCN compared to other state-of-the-art GCNs, particularly in scenarios where the information the digons carry is crucial to successfully address the task at hand.

[EN] High fuel injection pressure (>500 bar) in direct injection gasoline engines is an important means to reduce particulate emissions. While decades of fuel spray research has dramatically advanced the understanding high-pressure diesel fuel sprays, few studies focus on high-pressure gasoline sprays. The objective of this work was to quantify the effects of different injector nozzle geometries on important high-pressure gasoline spray characteristics including injection mass flow rate, momentum flux, and spray imaging at evaporative and non-evaporative conditions. Three categories of nozzle internal geometry were evaluated: inlet rounding; converging-, diverging-, and straight-cylindrical internal flow passages; and different nozzle outlet diameters. Reference grade gasoline was used at injection pressures of 600, 900, 1200, and 1500 bar at chamber pressures from 1 to 30 bar and chamber temperatures from 293 to 800 K. Two fuel injector temperatures of 293 K and 363 K were studied. The mass and momentum measurements were used to quantify differences in injector geometry as well as to evaluate for effects of cavitation. The visualization data were analyzed to determine spray penetration and spray angle development for a broad range of operating and state conditions. The results showed internal flow significantly impacts injector performance, where nozzles with inlet rounding resulted in 20% higher mass flow rate compared with straight cylindrical nozzles. Higher fuel injector temperatures also increased mass flow rate by up to 5%. Spray momentum coefficients showed a linear relationship with cavitation number indicating all nozzles were cavitating at all conditions tested. Trends in fuel spray penetration and spray angle development were similar to those observed previously for diesel sprays, which was unexpected given the significant differences in thermal-physical properties of the fuels. Chamber pressure had the strongest influence on penetration distance, and th

Fuzzy Description Logics (DLs) are used to represent and reason about vague and imprecise knowledge that is inherent to many application domains. It was recently shown that the complexity of reasoning in finitely-valued fuzzy DLs is often not higher than that of the underlying classical DL. We show that this does not hold for fuzzy extensions of the light-weight DL EL, which is used in many biomedical ontologies, under the Lukasiewicz semantics. The complexity of reasoning increases from PTime to ExpTime, even if only one additional truth value is introduced. The same lower bound holds also for infinitely-valued Lukasiewicz extensions of EL.

Research on agent communication languages has typically taken the speech acts paradigm as its starting point. Despite their manifest attractions, speech-act models of communication have several serious disadvantages as a foundation for communication in artificial agent systems. In particular, it has proved to be extremely difficult to give a satisfactory semantics to speech-act based agent communication languages. In part, the problem is that speech-act semantics typically make reference to the "mental states" of agents (their beliefs, desires, and intentions), and there is in general no way to attribute such attitudes to arbitrary computational agents. In addition, agent programming languages have only had their semantics formalised for abstract, stand-alone versions, neglecting aspects such as communication primitives. With respect to communication, implemented agent programming languages have tended to be rather ad hoc. This paper addresses both of these problems, by giving semantics to speech-act based messages received by an AgentSpeak agent. AgentSpeak is a logic-based agent programming language which incorporates the main features of the PRS model of reactive planning systems. The paper builds upon a structural operational semantics to AgentSpeak that we developed in previous work. The main contributions of this paper are as follows: an extension of our earlier work on the theoretical foundations of AgentSpeak interpreters; a computationally grounded semantics for (the core) performatives used in speech-act based agent communication languages; and a well-defined extension of AgentSpeak that supports agent communication.

This paper studies bilateral multi-issue negotiation between self-interested autonomous agents. Now, there are a number of different procedures that can be used for this process; the three main ones being the package deal procedure in which all the issues are bundled and discussed together, the simultaneous procedure in which the issues are discussed simultaneously but independently of each other, and the sequential procedure in which the issues are discussed one after another. Since each of them yields a different outcome, a key problem is to decide which one to use in which circumstances. Specifically, we consider this question for a model in which the agents have time constraints (in the form of both deadlines and discount factors) and information uncertainty (in that the agents do not know the opponents utility function). For this model, we consider issues that are both independent and those that are interdependent and determine equilibria for each case for each procedure. In so doing, we show that the package deal is in fact the optimal procedure for each party. We then go on to show that, although the package deal may be computationally more complex than the other two procedures, it generates Pareto optimal outcomes (unlike the other two), it has similar earliest and latest possible times of agreement to the simultaneous procedure (which is better than the sequential procedure), and that it (like the other two procedures) generates a unique outcome only under certain conditions (which we define).

We investigate the complexity of axiom pinpointing for different members of the DL-Lite family of Description Logics. More precisely, we consider the problem of enumerating all minimal subsets of a given DL-Lite knowledge base that have a given consequence. We show that for the DL-Litecore, DL-Litekrom and DL-Litehorn fragments such minimal subsets are efficiently enumerable with polynomial delay, but for the DL-Litebool fragment they cannot be enumerated in output polynomial time unless P = NP. We also show that interestingly, for the DL-Litehorn fragment such minimal sets can be enumerated in reverse lexicographic order with polynomial delay, but it is not possible in the forward lexicographic order since computing the first one is already coNP-hard.

In this paper, we propose an automated approach to track and recognize daily activities. Any activity is represented in this research as a sequence of raw sensors data. These sequences are treated using statistical methods in order to discover activity patterns. However, as the way to perform an activity is usually not fixed but it changes and evolves, we propose an activity recognition method based on Evolving Systems.

Hans van Ditmarsch and Barteld Kooi (2008). Semantic results for ontic and epistemic change. In: G. Bonanno, W. van der Hoek and M. Wooldridge (editors). Logic and the Foundations of Game and Decision Theory (LOFT 7). Texts in Logic and Games 3, pp. 87-117, Amsterdam University Press, Amsterdam.

Hans van Ditmarsch and Barteld Kooi (2008). Semantic results for ontic and epistemic change. In: G. Bonanno, W. van der Hoek and M. Wooldridge (editors). Logic and the Foundations of Game and Decision Theory (LOFT 7). Texts in Logic and Games 3, pp. 87-117, Amsterdam University Press, Amsterdam.

In this paper we present a rich decision model for intelligent agents involved in multi issue negotiations. The model, grounded on information theory, takes into account the aspects of trust and preference to devise mechanisms to manage dialogues. The model supports the design of agents that aim to take informed decisions taking into account that which they have actually observed.

Emergent processes are non-routine, collaborative business processes whose execution is guided by the knowledge that emerges during a process instance. In so far as the process goal gives direction to conventional business processes, the continually evolving process knowledge gives direction to emergent processes. Emergent processes may involve informal interaction, and so there is a limit to the extent to which the processes can be "managed". The collaboration however can be managed. Managing collaboration needs an intelligent agent that is guided not by a process goal, but by observing the performance of the other agents. Each agent has process knowledge --- that is information either generated by the individual users or is extracted from the environment, and performance knowledge --- that describes how the other agents, together with their 'owners', perform --- including how reliable they are. The integrity of the information derived from past observations decays in time, and so they have an inference mechanism that can cope with information of decaying integrity. An agent is described that achieves this by using ideas from information theory. The agents' internal representation language is probabilistic first-order logic. They derive models of the other agents using entropy-based inference that is based on random worlds. Maximum entropy inference is used to construct these models that are then refreshed as new information is received using minimum relative entropy inference.

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