A network embedding consists of a vector representation for each node in the network. Network embeddings have shown their usefulness in node classification and visualization in many real-world application domains, such as social networks and web networks. While directed networks with text associated with each node, such as citation networks and software package dependency networks, are commonplace, to the best of our knowledge, their embeddings have not been specifically studied. In this paper, we create PCT ADW-1 and PCT ADW-2, two algorithms based on NNs that learn em-beddings of directed networks with text associated with each node. We create two new labeled directed networks with text-associated node: The package dependency networks in two popular GNU/Linux distributions, Debian and Fedora. We experimentally demonstrate that the embeddings produced by our NNs resulted in node classification with better quality than those of various baselines on these two networks. We observe that there exist systematic presence of analogies (similar to those in word embeddings) in the network embed-dings of software package dependency networks. To the best of our knowledge, this is the first time that such a systematic presence of analogies is observed in network and document embeddings. This may potentially open up a new venue for better understanding networks and documents algorith-mically using their embeddings as well as for better human understanding of network and document embeddings.

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We introduce a novel class of adjustment rules for a collection of beliefs. This is an extension of Lewis' imaging to absorb probabilistic evidence in generalized settings. Unlike standard tools for belief revision, our proposal may be used when information is inconsistent with an agent's belief base. We show that the functionals we introduce are based on the imaginary counterpart of probability kinematics for standard belief revision, and prove that, under certain conditions, all standard postulates for belief revision are satisfied.

Darwiche and Pearl's seminal 1997 article outlined a number of baseline principles for a logic of iterated belief revision. These principles, the DP postulates, have been supplemented in a number of alternative ways. Most of the suggestions made have resulted in a form of `reductionism' that identifies belief states with orderings of worlds. However, this position has recently been criticised as being unacceptably strong. Other proposals, such as the popular principle (P), aka `Independence', characteristic of `admissible' revision operators, remain commendably more modest. In this paper, we supplement both the DP postulates and (P) with a number of novel conditions. While the DP postulates constrain the relation between a prior and a posterior conditional belief set, our new principles notably govern the relation between two posterior conditional belief sets obtained from a common prior by different revisions. We show that operators from the resulting family, which subsumes both lexicographic and restrained revision, can be represented as relating belief states that are associated with a `proper ordinal interval' (POI) assignment, a structure more fine-grained than a simple ordering of worlds. We close the paper by noting that these operators satisfy iterated versions of a large number of AGM era postulates, including Superexpansion, that are not sound for admissible operators in general.

Dependence is an important concept for many tasks in artificial intelligence. A task can be executed more efficiently by discarding something independent from the task. In this paper, we propose two novel notions of dependence in propositional logic: formula-formula dependence and formula forgetting. The first is a relation between formulas capturing whether a formula depends on another one, while the second is an operation that returns the strongest consequence independent of a formula. We also apply these two notions in two well-known issues: belief update and conservative extension. Firstly, we define a new update operator based on formula-formula dependence. Furthermore, we reduce conservative extension to formula forgetting.

In partially observed environments, it can be useful for a human to provide the robot with declarative information that represents probabilistic relational constraints on properties of objects in the world, augmenting the robot's sensory observations. For instance, a robot tasked with a search-and-rescue mission may be informed by the human that two victims are probably in the same room. An important question arises: how should we represent the robot's internal knowledge so that this information is correctly processed and combined with raw sensory information? In this paper, we provide an efficient belief state representation that dynamically selects an appropriate factoring, combining aspects of the belief when they are correlated through information and separating them when they are not. This strategy works in open domains, in which the set of possible objects is not known in advance, and provides significant improvements in inference time over a static factoring, leading to more efficient planning for complex partially observed tasks.

Goal Recognition Design (GRD) is the task of redesigning environments (either physical or virtual) to allow efficient online goal recognition. In this work we formulate the redesign problem as an optimization problem, aiming at early goal recognition. To this end, we use a measure of worst case distinctiveness (wcd), which represents the maximal number of steps an agent may take before his goal is revealed. With the objective ofminimizing wcd, we construct a search space in which each node in the space is a goal recognition model (one of which is the original model given as input) and one can move from one model to another by applying a model modification, chosen from a set of allowed modifications given as input. Our specific contribution in this work includes the specification of a class of modifications for which we can prune the search space using strong stubborn sets. Such positioning allows reducing the computational overhead of design while preserving completeness. We show that the proposed modification class generalizes previous works in goal recognition design and enriches the state-of-the-art with new modifications for which strong stubborn set pruning is safe. We support our approach by an empirical evaluation that reveals the performance gain brought by the proposed pruning strategy in different goal recognition design settings.

Belief change within the framework of fragments of propositional logic is one of the main and recent challenges in the knowledge representation research area. While previous research works focused on belief revision, belief merging, and belief contraction, the problem of belief update within fragments of classical logic has not been addressed so far. In the context of revision, it has been proposed to refine existing operators so that they operate within propositional fragments, and that the result of revision remains in the fragment under consideration. This approach is not restricted to the Horn fragment but also applicable to other propositional fragments like Krom and affine fragments. We generalize this notion of refinement to any belief change operator. We then focus on a specific belief change operation, namely belief update. We investigate the behavior of the refined update operators with respect to satisfaction of the KM postulates and highlight differences between revision and update in this context.

Recent approaches to goal recognition have progressively relaxed the assumptions about the amount and correctness of domain knowledge and available observations, yielding accurate and efficient algorithms. These approaches, however, assume completeness and correctness of the domain theory against which their algorithms match observations: this is too strong for most real-world domains. In this paper, we develop goal recognition techniques that are capable of recognizing goals using \textit{incomplete} (and possibly incorrect) domain theories. We show the efficiency and accuracy of our approaches empirically against a large dataset of goal and plan recognition problems with incomplete domains.

It is well known that the problems of stochastic planning and probabilistic inference are closely related. This paper makes several contributions in this context for factored spaces where the complexity of solutions is challenging. First, we analyze the recently developed SOGBOFA heuristic, which performs stochastic planning by building an explicit computation graph capturing an approximate aggregate simulation of the dynamics. It is shown that the values computed by this algorithm are identical to the approximation provided by Belief Propagation (BP). Second, as a consequence of this observation, we show how ideas on lifted BP can be used to develop a lifted version of SOGBOFA. Unlike implementations of lifted BP, Lifted SOGBOFA has a very simple implementation as a dynamic programming version of the original graph construction. Third, we show that the idea of graph construction for aggregate simulation can be used to solve marginal MAP (MMAP) problems in Bayesian networks, where MAP variables are constrained to be at roots of the network. This yields a novel algorithm for MMAP for this subclass. An experimental evaluation illustrates the advantage of Lifted SOGBOFA for planning.