We provide an algorithm to compute near-optimal strategies for the Cultaptation social learning game. We show that the strategies produced by our algorithm are near-optimal, both in their expected utility and their expected reproductive success. We show how our algorithm can be used to provide insight into evolutionary conditions under which learning is best done by copying others, versus the conditions under which learning is best done by trial-and-error.

We present an approach to identifying concurrent patterns of behavior in in-sample temporal factor training data that precede Events of Interest (EoIs). We also present how to use discovered patterns to forecast EoIs in out-of-sample test data. The forecasting methodology is based on matching entities' observed behaviors to patterns discovered in retrospective data. This pattern concept is a generalization of previous pattern definitions. The new pattern concept, based around patterns observed in trends of factor data is based on a finite-state model where observed, sustained trends in a factor map to pattern states. Discovered patterns can be used as a diagnostic tool to better understand the dynamic conditions leading up to specific Event of Interest occurrences and hint at underlying causal structures leading to onsets and terminations of socio-political violence. We present a computationally efficient data-mining method to discover trend patterns. We give an example of using our pattern forecasting methodology to correctly forecast the advent and cessation of ethnic-religious violence in nation states with a low false-alarm rate.

In this paper we introduce SCARE — the Spatial Cultural Abductive Reasoning Engine, which solves spatial abduction problems (Shakarian, Subrahmanian, and Sapino 2009). We review results of SCARE for activities by Iranian-sponsored “Special Groups” (Kagan, Kagan, and Pletka 2008) operating throughout the Baghdad urban area and compare these findings with new experiments where we predict IED cache sites of the Special Groups in Sadr City. We find that by localizing the spatial abduction problem to a smaller area we obtain greater accuracy - predicting cache sites within 0.33 km as opposed to 0.72 km for all of Baghdad. We suspect that local factors of physical and cultural geography impact reasoning with spatial abduction for this problem.

After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the question what is the most likely label of a given unseen data point. However, most methods will provide no answer why the model predicted the particular label for a single instance and what features were most influential for that particular instance. The only method that is currently able to provide such explanations are decision trees. This paper proposes a procedure which (based on a set of assumptions) allows to explain the decisions of any classification method.

Slow feature analysis (SFA) is a method for extracting slowly varying features from a quickly varying multidimensional signal. An open source Matlab-implementation sfa-tk makes SFA easily useable. We show here that under certain circumstances, namely when the covariance matrix of the nonlinearly expanded data does not have full rank, this implementation runs into numerical instabilities. We propse a modified algorithm based on singular value decomposition (SVD) which is free of those instabilities even in the case where the rank of the matrix is only less than 10% of its size. Furthermore we show that an alternative way of handling the numerical problems is to inject a small amount of noise into the multidimensional input signal which can restore a rank-deficient covariance matrix to full rank, however at the price of modifying the original data and the need for noise parameter tuning.

This survey is an introduction to positive definite kernels and the set of methods they have inspired in the machine learning literature, namely kernel methods. We first discuss some properties of positive definite kernels as well as reproducing kernel Hibert spaces, the natural extension of the set of functions $\{k(x,\cdot),x\in\mathcal{X}\}$ associated with a kernel $k$ defined on a space $\mathcal{X}$. We discuss at length the construction of kernel functions that take advantage of well-known statistical models. We provide an overview of numerous data-analysis methods which take advantage of reproducing kernel Hilbert spaces and discuss the idea of combining several kernels to improve the performance on certain tasks. We also provide a short cookbook of different kernels which are particularly useful for certain data-types such as images, graphs or speech segments.

The fundamental laws of quantum world upsets the logical foundation of classic physics. They are completely counter-intuitive with many bizarre behaviors. However, this paper shows that they may make sense from the perspective of a general decision-optimization principle for cooperation. This principle also offers a generalization of Nash equilibrium, a key concept in game theory, for better payoffs and stability of game playing.

Logics of knowledge based on possible-world semantics are u seful in many areas of knowledge representation and reasoning, ranging from security t o distributed computing to game theory. In these models, an agent is said to know a fact ϕ if ϕ is true in all the worlds she considers possible. While reasoning about knowledge with t his semantics has proved useful, as is well known, it suffers from what is known in the literature as the logical omniscience problem: under possible-world semantics, agents know all t autologies and know the logical consequences of their knowledge. While logical omniscience is certainly not always an issue, in many applications it is. For example, in the context of distributed computing, we are interested in polynomial-time algorithms, although in some cases the knowledge needed to p erform optimally may require calculations that cannot be performed in polynomial time (u nless P=NP) [Moses and Tuttle 1988]; in the context of security, we may want to reason about computationally bounded adversaries who cannot factor a large composite number, and thus cannot be logically omniscient; in game theory, we may be interested in the impac t of computational resources on solution concepts (for example, what will agents do if com puting a Nash equilibrium is difficult). Not surprisingly, many approaches for dealing with the logi cal omniscience problem have been suggested (see [Fagin, Halpern, Moses, and Vardi 1 995, Chapter 9] and [Moreno 1998]).

The present paper investigates consequence relations that are both non-monotonic and paraconsistent. More precisely, we put the focus on preferential consequence relations, i.e. those relations that can be defined by a binary preference relation on states labelled by valuations. We worked with a general notion of valuation that covers e.g. the classical valuations as well as certain kinds of many-valued valuations. In the many-valued cases, preferential consequence relations are paraconsistant (in addition to be non-monotonic), i.e. they are capable of drawing reasonable conclusions which contain contradictions. The first purpose of this paper is to provide in our general framework syntactic characterizations of several families of preferential relations. The second and main purpose is to provide, again in our general framework, characterizations of several families of preferential discriminative consequence relations. They are defined exactly as the plain version, but any conclusion such that its negation is also a conclusion is rejected (these relations bring something new essentially in the many-valued cases).

The on-line shortest path problem is considered under various mode ls of partial monitoring. Given a weighted directed acyclic graph whose edge weights can c hange in an arbitrary (adversarial) way, a decision maker has to choose in each round of a game a path between two distinguished vertices such that the loss of the chosen path (defin ed as the sum of the weights of its composing edges) be as small as possible. In a setting generalizing the multi-armed bandit problem, after choosing a path, the decision maker learns only the w eights of those edges that belong to the chosen path. For this problem, an algorithm is given who se average cumulative loss in n rounds exceeds that of the best path, matched off-line to the ent ire sequence of the edge weights, by a quantity that is proportional to 1 / n and depends only polynomially on the number of edges of the graph. The algorithm can be implemented with linear complexity in the number of rounds n and in the number of edges. An extension to the so-called label efficie nt setting is also given, in which the decision maker is informed about the w eights of the edges corresponding to the chosen path at a total of m n time instances. Another extension is shown where the decision maker competes against a time-varying pa th, a generalization of the problem of tracking the best expert. A version of the multi-armed b andit setting for shortest path is also discussed where the decision maker learns only the total weight of the chosen path but not the weights of the individual edges on the path. Applications to routing in packet switched networks along with simulation results are also presented.