The Fundamental Nature of the Log Loss Function

The standard loss functions used in the literature on probabilistic prediction are the log loss function, the Brier loss function, and the spherical loss function; however, any computable proper loss function can be used for comparison of prediction algorithms. This note shows that the log loss function is most selective in that any prediction algorithm that is optimal for a given data sequence (in the sense of the algorithmic theory of randomness) under the log loss function will be optimal under any computable proper mixable loss function; on the other hand, there is a data sequence and a prediction algorithm that is optimal for that sequence under either of the two other standard loss functions but not under the log loss function.Comment: 12 page

On environment difficulty and discriminating power

The final publication is available at Springer via http://dx.doi.org/10.1007/s10458-014-9257-1This paper presents a way to estimate the difficulty and discriminating power of any task instance. We focus on a very general setting for tasks: interactive (possibly multiagent) environments where an agent acts upon observations and rewards. Instead of analysing the complexity of the environment, the state space or the actions that are performed by the agent, we analyse the performance of a population of agent policies against the task, leading to a distribution that is examined in terms of policy complexity. This distribution is then sliced by the algorithmic complexity of the policy and analysed through several diagrams and indicators. The notion of environment response curve is also introduced, by inverting the performance results into an ability scale. We apply all these concepts, diagrams and indicators to two illustrative problems: a class of agent-populated elementary cellular automata, showing how the difficulty and discriminating power may vary for several environments, and a multiagent system, where agents can become predators or preys, and may need to coordinate. Finally, we discuss how these tools can be applied to characterise (interactive) tasks and (multi-agent) environments. These characterisations can then be used to get more insight about agent performance and to facilitate the development of adaptive tests for the evaluation of agent abilities.I thank the reviewers for their comments, especially those aiming at a clearer connection with the field of multi-agent systems and the suggestion of better approximations for the calculation of the response curves. The implementation of the elementary cellular automata used in the environments is based on the library 'CellularAutomaton' by John Hughes for R [58]. I am grateful to Fernando Soler-Toscano for letting me know about their work [65] on the complexity of 2D objects generated by elementary cellular automata. I would also like to thank David L. Dowe for his comments on a previous version of this paper. This work was supported by the MEC/MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, the COST - European Cooperation in the field of Scientific and Technical Research IC0801 AT, and the REFRAME project, granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA), and funded by the Ministerio de Economia y Competitividad in Spain (PCIN-2013-037).José Hernández-Orallo (2015). On environment difficulty and discriminating power. Autonomous Agents and Multi-Agent Systems. 29(3):402-454. https://doi.org/10.1007/s10458-014-9257-1S402454293Anderson, J., Baltes, J., & Cheng, C. T. (2011). 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Can Machine Intelligence be Measured in the Same Way as Human intelligence?

In recent years the number of research projects on computer programs solving human intelligence problems in artificial intelligence (AI), artificial general intelligence, as well as in Cognitive Modelling, has significantly grown. One reason could be the interest of such problems as benchmarks for AI algorithms. Another, more fundamental, motivation behind this area of research might be the (implicit) assumption that a computer program that successfully can solve human intelligence problems has human-level intelligence and vice versa. This paper analyses this assumption

Drift as a Force of Evolution: A Manipulationist Account

Can evolutionary theory be properly characterised as a “theory of forces”, like Newtonian mechanics? One common criticism to this claim concerns the possibility to conceive genetic drift as a causal process endowed by a specific magnitude and direction. In this article, we aim to offer an original response to this criticism by pointing out a connection between the notion of force and the notion of explanatory depth, as depicted in Hitchcock and Woodward’s manipulationist account of causal explanation. In a nutshell, our argument is that, since force-explanations can be consistently reframed as deep explanations and vice versa, and the notion of drift can be characterised in manipulationist terms as constitutively intervening in evolutionary deep explanations, then drift-explanations can be consistently reframed as force-explanations, and drift can be properly considered as a force of evolution. Insofar as similar considerations may be extended also to other evolutionary factors – chiefly selection –, our analysis offers an important support to the claim that evolutionary theory is a theory of forces.info:eu-repo/semantics/publishedVersio

Evaluation in artificial intelligence: From task-oriented to ability-oriented measurement

The final publication is available at Springer via http://dx.doi.org/ 10.1007/s10462-016-9505-7.The evaluation of artificial intelligence systems and components is crucial for the progress of the discipline. In this paper we describe and critically assess the different ways AI systems are evaluated, and the role of components and techniques in these systems. We first focus on the traditional task-oriented evaluation approach. We identify three kinds of evaluation: human discrimination, problem benchmarks and peer confrontation. We describe some of the limitations of the many evaluation schemes and competitions in these three categories, and follow the progression of some of these tests. We then focus on a less customary (and challenging) ability-oriented evaluation approach, where a system is characterised by its (cognitive) abilities, rather than by the tasks it is designed to solve. We discuss several possibilities: the adaptation of cognitive tests used for humans and animals, the development of tests derived from algorithmic information theory or more integrated approaches under the perspective of universal psychometrics. We analyse some evaluation tests from AI that are better positioned for an ability-oriented evaluation and discuss how their problems and limitations can possibly be addressed with some of the tools and ideas that appear within the paper. Finally, we enumerate a series of lessons learnt and generic guidelines to be used when an AI evaluation scheme is under consideration.I thank the organisers of the AEPIA Summer School On Artificial Intelligence, held in September 2014, for giving me the opportunity to give a lecture on 'AI Evaluation'. This paper was born out of and evolved through that lecture. The information about many benchmarks and competitions discussed in this paper have been contrasted with information from and discussions with many people: M. Bedia, A. Cangelosi, C. Dimitrakakis, I. GarcIa-Varea, Katja Hofmann, W. Langdon, E. Messina, S. Mueller, M. Siebers and C. Soares. Figure 4 is courtesy of F. Martinez-Plumed. Finally, I thank the anonymous reviewers, whose comments have helped to significantly improve the balance and coverage of the paper. This work has been partially supported by the EU (FEDER) and the Spanish MINECO under Grants TIN 2013-45732-C4-1-P, TIN 2015-69175-C4-1-R and by Generalitat Valenciana PROMETEOII2015/013.José Hernández-Orallo (2016). Evaluation in artificial intelligence: From task-oriented to ability-oriented measurement. 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Identifying Flexible Pool Pumps Suitable for Distributed Demand Response Schemes

Demand response will be an important tool as non-dispatchable generation is added to the grid. Swimming pool filtration pumps are a promising appliance for the grid operator to control because, unlike air conditioners, their time of operation can be shifted by a day without affecting their primary role. Although many customers set and forget the timer settings, this paper studies smart meter data and demonstrates that many change the settings frequently. A "care index" is proposed that quantifies how much a customer appears to care about the precise timing of pump operation, which indicates how willing they are likely to be to relinquish control to the grid operator

Role of the NO-cGMP pathway in the muscarinic regulation of the L-type Ca2+ current in human atrial myocytes

The whole-cell patch-clamp technique was used to examine the participation of nitric oxide synthase (NOS) and soluble guanylyl cyclase in the muscarinic regulation of the L-type Ca2+ current (ICa) in freshly isolated human atrial myocytes.Acetylcholine (ACh, 1 μM) decreased basal ICa by 39.1 ± 5.5 % (n= 8) under control conditions, and by 38.0 ± 6.1 % (n= 6) in the presence of 1H-[1,2,4]oxadiazolo[4,3-a]quinoxaline-1-one (ODQ, 10 μM), a potent guanylyl cyclase inhibitor, and NG-monomethyl-L-arginine (L-NMMA, 1 mM), a competitive NOS inhibitor. L-NMMA alone had no effect on ICa, whilst ODQ increased ICa in 50 % of the cells.The accentuated antagonism of ACh on ICa, i.e. its ability to antagonize the stimulatory effect of β-adrenergic agonists and, by extension, of other cAMP-elevating agents, was examined after the current was stimulated by either the β-adrenergic agonist isoprenaline (Iso) or serotonin (5-HT). ACh (100 nM or 1 μM) completely blocked the stimulatory effects of 10 nM Iso or 10 nM 5-HT on ICa.Extracellular application of Methylene Blue (MBlue, 10 μM), a guanylyl cyclase inhibitor, antagonized the inhibitory effect of 1 μM ACh on Iso- or 5-HT-stimulated ICa. However, this effect was overcome by a 100-fold higher ACh concentration and was not mimicked by an intracellular application of MBlue.Inhibition of NOS and soluble guanylyl cyclase activities by addition of ODQ (10 μM) and L-NMMA (1 mM) to both extracellular and intracellular solutions, or by a 2 h pre-incubation of the cells with these inhibitors, modified neither the Iso (10 nM) response nor the inhibitory effect of ACh (100 nM or 1 μM) on Iso-stimulated ICa.Extracellular application of the NO donor SNAP (S-nitroso-N-acetyl-d,l-penicillamine) at 100 nM produced a stimulatory effect on ICa in control conditions. This stimulatory effect was abolished by intracellular MBlue (20 μM) or by intracellular and extracellular application of ODQ (10 μM) in combination with L-NMMA (1 mM).We conclude that the NO-cGMP pathway does not contribute significantly to the muscarinic regulation of ICa in human atrial myocytes