MDL Convergence Speed for Bernoulli Sequences

The Minimum Description Length principle for online sequence estimation/prediction in a proper learning setup is studied. If the underlying model class is discrete, then the total expected square loss is a particularly interesting performance measure: (a) this quantity is finitely bounded, implying convergence with probability one, and (b) it additionally specifies the convergence speed. For MDL, in general one can only have loss bounds which are finite but exponentially larger than those for Bayes mixtures. We show that this is even the case if the model class contains only Bernoulli distributions. We derive a new upper bound on the prediction error for countable Bernoulli classes. This implies a small bound (comparable to the one for Bayes mixtures) for certain important model classes. We discuss the application to Machine Learning tasks such as classification and hypothesis testing, and generalization to countable classes of i.i.d. models.Comment: 28 page

Probabilities on Sentences in an Expressive Logic

Automated reasoning about uncertain knowledge has many applications. One difficulty when developing such systems is the lack of a completely satisfactory integration of logic and probability. We address this problem directly. Expressive languages like higher-order logic are ideally suited for representing and reasoning about structured knowledge. Uncertain knowledge can be modeled by using graded probabilities rather than binary truth-values. The main technical problem studied in this paper is the following: Given a set of sentences, each having some probability of being true, what probability should be ascribed to other (query) sentences? A natural wish-list, among others, is that the probability distribution (i) is consistent with the knowledge base, (ii) allows for a consistent inference procedure and in particular (iii) reduces to deductive logic in the limit of probabilities being 0 and 1, (iv) allows (Bayesian) inductive reasoning and (v) learning in the limit and in particular (vi) allows confirmation of universally quantified hypotheses/sentences. We translate this wish-list into technical requirements for a prior probability and show that probabilities satisfying all our criteria exist. We also give explicit constructions and several general characterizations of probabilities that satisfy some or all of the criteria and various (counter) examples. We also derive necessary and sufficient conditions for extending beliefs about finitely many sentences to suitable probabilities over all sentences, and in particular least dogmatic or least biased ones. We conclude with a brief outlook on how the developed theory might be used and approximated in autonomous reasoning agents. Our theory is a step towards a globally consistent and empirically satisfactory unification of probability and logic.Comment: 52 LaTeX pages, 64 definiton/theorems/etc, presented at conference Progic 2011 in New Yor

Self-Modification of Policy and Utility Function in Rational Agents

Any agent that is part of the environment it interacts with and has versatile actuators (such as arms and fingers), will in principle have the ability to self-modify -- for example by changing its own source code. As we continue to create more and more intelligent agents, chances increase that they will learn about this ability. The question is: will they want to use it? For example, highly intelligent systems may find ways to change their goals to something more easily achievable, thereby `escaping' the control of their designers. In an important paper, Omohundro (2008) argued that goal preservation is a fundamental drive of any intelligent system, since a goal is more likely to be achieved if future versions of the agent strive towards the same goal. In this paper, we formalise this argument in general reinforcement learning, and explore situations where it fails. Our conclusion is that the self-modification possibility is harmless if and only if the value function of the agent anticipates the consequences of self-modifications and use the current utility function when evaluating the future.Comment: Artificial General Intelligence (AGI) 201

Disentangling Genetic and Prenatal Maternal Effects on Offspring Size and Survival

This is the author accepted manuscript. The final version is available from University of Chicago Press via the DOI in this record.Organizational processes during prenatal development can have long-term effects on an individual's phenotype. Because these early developmental stages are sensitive to environmental influences, mothers are in a unique position to alter their offspring's phenotype by differentially allocating resources to their developing young. However, such prenatal maternal effects are difficult to disentangle from other forms of parental care, additive genetic effects, and/or other forms of maternal inheritance, hampering our understanding of their evolutionary consequences. Here we used divergent selection lines for high and low prenatal maternal investment and their reciprocal line crosses in a precocial bird-the Japanese quail (Coturnix japonica)-to quantify the relative importance of genes and prenatal maternal effects in shaping offspring phenotype. Maternal but not paternal origin strongly affected offspring body size and survival throughout development. Although the effects of maternal egg investment faded over time, they were large at key life stages. Additionally, there was evidence for other forms of maternal inheritance affecting offspring phenotype at later stages of development. Our study is among the first to successfully disentangle prenatal maternal effects from all other sources of confounding variation and highlights the important role of prenatal maternal provisioning in shaping offspring traits closely linked to fitness.The study was financially supported by the Swiss National Science Foundation (PP00P3_128386 and PP00P3_157455 to B.T.)

Electron-phonon interaction in the solid form of the smallest fullerene C20_{20}

The electron-phonon coupling of a theoretically devised carbon phase made by assembling the smallest fullerenes C$_{20}$ is calculated from first principles. The structure consists of C$_{20}$ cages in an {\it fcc} lattice interlinked by two bridging carbon atoms in the interstitial tetrahedral sites ({\it fcc}-C$_{22}$). The crystal is insulating but can be made metallic by doping with interstitial alkali atoms. In the compound NaC$_{22}$ the calculated coupling constant $\lambda/N(0)$ is 0.28 eV, a value much larger than in C$_{60}$, as expected from the larger curvature of C$_{20}$. On the basis of the McMillan's formula, the calculated $\lambda$=1.12 and a $\mu^*$ assumed in the range 0.3-0.1 a superconducting T$_c$ in the range 15-55 K is predicted.Comment: 7 page