We present a method for context-dependent and incremental intention recognition by means of incrementally constructing a Bayesian Network (BN) model as more actions are observed. It is achieved with the support of a knowledge base of readily maintained and constructed fragments of BNs. The simple structure of the fragments enables to easily and efficiently acquire the knowledge base, either from domain experts or automatically from a plan corpus. We exhibit experimental results improvement for the Linux Plan corpus. For additional experimentation, new plan corpora for the iterated Prisoner’s Dilemma are created. We show that taking into account contextual information considerably increases intention recognition performance. 1

The purpose of this experimental work is to compare the performance of three programs capable of calculating LOD score: Morgan, Superlink and SampleSearch [1]. SampleSearch is a general purpose algorithm for finding probability of evidence in a Bayesian network while MOrgan and Superlink are specialized programs aimed at estimating LOD score. Superlink is

Abstract. Many real world problems can be modeled using a combination of hard and soft constraints. Markov Logic is a highly expressive language which represents the underlying constraints by attaching realvalued weights to formulas in first order logic. The weight of a formula represents the strength of the corresponding constraint. Hard constraints are represented as formulas with infinite weight. The theory is compiled into a ground Markov network over which probabilistic inference can be done. For many problems, hard constraints pose a significant challenge to the probabilistic inference engine. However, solving the hard constraints (partially or fully) before hand outside of the probabilistic engine can hugely simplify the ground Markov network and speed probabilistic inference. In this work, we propose a generalized arc consistency algorithm that prunes the domains of predicates by propagating hard constraints. Our algorithm effectively performs unit propagation at a lifted level, avoiding the need to explicitly ground the hard constraints during the pre-processing phase, yielding a potentially exponential savings in space and time. Our approach results in much simplified domains, thereby, making the inference significantly more efficient both in terms of time and memory. Experimental evaluation over one artificial and two real-world datasets show the benefit of our approach. 1

Itiswellknownthatcomputingrelativeapproximationsofweightedcountingqueries such as the probability of evidence in a Bayesian network, the partition function of a Markov network, and the number of solutions of a constraint satisfaction problem is NP-hard. In this paper, we settle therefore on an easier problem of computing highconfidence lower bounds and propose an algorithm based on importance sampling and Markov inequality for it. However, a straight-forward application of Markov inequality often yields poor lower bounds because it uses only one sample. We therefore propose several new schemes that extend it to multiple samples. Empirically, we show that our new schemes are quite powerful, often yielding substantially higher (better) lower bounds than state-of-the-art schemes. 1