Activity recognition plays an important role in many areas such as smart environments by offering unprecedented opportunities for assisted living, automation, security and energy efficiency. It’s also an essential component for planning and plan recognition in smart environments. One challenge of activity recognition is the need for collecting and annotating huge amounts of data for each new physical setting in order to be able to carry out the conventional activity discovery and recognition algorithms. This extensive initial phase of data collection and annotation results in a prolonged installation process and excessive time investment for each new space. In this paper we propose a new method of transferring learned knowledge of activities to a new physical space in order to leverage the learning process in the new environment. Our method called ”Home to Home Transfer Learning” (HHTL) is based on using a semi EM framework and modeling activities using structural, temporal and spatial features. This method allows us to avoid the tedious task of collecting and labeling huge amounts of data in the target space, and allows for a more accelerated and more scalable deployment cycle in the real world. It also allows us to exploit the insights learned in previous spaces. To validate our algorithms, we use the data collected in several smart apartments with different physical layouts.

It is still unclear what metacognition is. Two main theories about metacognition are reviewed, each of which claims to provide a better explanation of the phenomenon, while discrediting the other theory as inappropriate. My claim is that in order to do justice to the complex phenomenon of metacognition, we must distinguish two levels of this capacity. It can be shown that each of these theories has been trying to explain only one of the two levels and that, consequently, the conflict between them can be dissolved. Finally, I characterize each level and explain some of their interactions.

Lifting can greatly reduce the cost of inference on first-order probabilistic models, but constructing the lifted network can itself be quite costly. In addition, the minimal lifted network is often very close in size to the fully propositionalized model; lifted inference yields little or no speedup in these situations. In this paper, we address both these problems. We propose a compact hypercube-based representation for the lifted network, which can greatly reduce the cost of lifted network construction. We also present two methods for approximate lifted network construction, which groups together similar but distinguishable objects and treats them as if they were identical. This can greatly reduce the size of the lifted network as well as the time required for lifted network construction, but potentially at some cost to accuracy. The coarseness of the approximation can be adjusted depending on the accuracy required, and we can bound the resulting error. Experiments on six domains show great efficiency gains with only minor loss in accuracy.

Learning the relational structure of a domain is a fundamental problem in statistical relational learning. The deep transfer algorithm of Davis and Domingos attempts to improve structure learning in Markov logic networks by harnessing the power of transfer learning, using the second-order structural regularities of a source domain to bias the structure search process in a target domain. We propose that the clique-scoring process which discovers these second-order regularities constitutes a novel standalone method for learning the structure of Markov logic networks, and that this fact, rather than the transfer of structural knowledge across domains, accounts for much of the performance benefit observed via the deep transfer process. This claim is supported by experiments in which we find that clique scoring within a single domain often produces results equaling or surpassing the performance of deep transfer incorporating external knowledge, and also by explicit algorithmic similarities between deep transfer and other structure learning techniques.

The quality and nature of knowledge that can be found by an automated knowledge-extraction system depends on its inputs. For systems that learn by reading text, the Web offers a breadth of topics and currency, but it also presents the problems of dealing with casual, unedited writing, non-textual inputs, and the mingling of languages. The results of extraction using the KNEXT system on two Web corpora — Wikipedia and a collection of weblog entries — indicate that, with automatic filtering of the output, even ungrammatical writing on arbitrary topics can yield an extensive knowledge base, which human judges find to be of good quality, with propositions receiving an average score across both corpora of 2.34 (where the range is 1 to 5 and lower is better) versus 3.00 for unfiltered output from the same sources.

Past research has shown that when tree-structured background knowledge is available, it can be exploited to increase the efficiency of classification learning. When this kind of background knowledge is available, the problem becomes one of compositional classification. Of course, if the background knowledge contains errors, the quality of the learned hypothesis will suffer. In this paper we study the effect of faulty knowledge engineering on compositional classification learning. We present and analyze empirical results that show the impact on the quality of compositional classification learning as the quality of knowledge engineering is degraded.

We study the problem of achieving cooperation between two self-interested agents that play a sequence of randomly generated normal form games, each game played only once. To achieve cooperation we extend a model used to explain cooperative behavior by humans. We show how a modification of a pre-regularized particle filter can be used to detect the cooperation level of the opponent and play accordingly. We examine how properties of the games affect the ability of an agent to detect cooperation and explore the effects of different environments and different levels of conflict. We present results obtained in simulation on hundreds of randomly generated games.

Solving real-world problems using symbolic planning often requires a simplified formulation of the original problem, since certain subproblems cannot be represented at all or only in a way leading to inefficiency. For example, manipulation planning may appear as a subproblem in a robotic planning context or a packing problem can be part of a logistics task. In this paper we propose an extension of PDDL for specifying semantic attachments. This allows the evaluation of grounded predicates, the change of fluents and the calculation of durations by externally specified functions. Furthermore, we describe a general schema of integrating semantic attachments into forward-chaining planning systems and report on our experience of adding this extension to the planner Temporal Fast Downward. Finally, we present some preliminary experiments using semantic attachments.

In this paper, we introduce Bayesian Abductive Logic Programs (BALPs), a new formalism that integrates Bayesian Logic Programs (BLPs) and Abductive Logic Programming (ALP) for abductive reasoning. Like BLPs, BALPs also combine first-order logic and Bayesian networks. However, unlike BLPs that use logical deduction to construct Bayes nets, BALPs employ logical abduction. As a result, BALPs are more suited for solving problems like plan/activity recognition and diagnosis that require abductive reasoning. First, we present the necessary enhancements to BLPs in order to support logical abduction. Next, we apply BALPs to the task of plan recognition and demonstrate its efficacy on two data sets. We also compare the performance of BALPs with several existing approaches for abduction.

One approach to combining ASP and CP is to integrate There are several approaches to representing and solving theory-specific predicates into propositional formulas (motivated constraint satisfaction problems: constraint programming by SMT), and to extend the ASP solver's decision (CP; Dechter 2003, Rossi, van Beek, and Walsh 2006), answer engine with a higher level proof procedure (Baselice, set programming (ASP; Baral 2003), propositional satisfiability Bonatti, and Gelfond 2005; Mellarkod and Gelfond 2008; checking (SAT; Biere et al. 2009), its extension Gebser, Ostrowski, and Schaub 2009). However, the resulting to satisfiability modulo theories (SMT; Nieuwenhuis, Oliveras, systems have a number of limitations. First, they are and Tinelli 2006), and many more. Each has its particular tied to particular ASP and CP solvers. Second, the support strengths: for example, CP systems support global constraints, for global constraints is limited. Third, communication between ASP systems permit recursive definitions and offer the ASP and CP solver is restricted. Alternative techniques, default negation, whilst SAT solvers often exploit very such as reformulating constraints into ASP have received efficient implementations.