Robots assisting us in environments such as factories or homes must learn to make use of objects as tools to perform tasks, for instance, using a tray to carry objects. We consider the problem of learning common sense knowledge of when a tool may be useful and how its use may be composed with other tools to accomplish a high-level task instructed by a human. Specifically, we introduce a novel neural model, termed TOOLTANGO, that first predicts the next tool to be used, and then uses this information to predict the next action. We show that this joint model can inform learning of a fine-grained policy enabling the robot to use a particular tool in sequence and adds a significant value in making the model more accurate. TOOLTANGO encodes the world state, comprising objects and symbolic relationships between them, using a graph neural network and is trained using demonstrations from human teachers instructing a virtual robot in a physics simulator. The model learns to attend over the scene using knowledge of the goal and the action history, finally decoding the symbolic action to execute. Crucially, we address generalization to unseen environments where some known tools are missing, but unseen alternative tools are present. We show that by augmenting the representation of the environment with pre-trained embeddings derived from a knowledge-base, the model can generalize effectively to novel environments. Experimental results show at least 48.8-58.1% absolute improvement over the baselines in predicting successful symbolic plans for a simulated mobile manipulator in novel environments with unseen objects. This work takes a step in the direction of enabling robots to rapidly synthesize robust plans for complex tasks, particularly in novel settings.

We study a novel task of numerical relation extraction with the goal of extracting relations where one of the arguments is a number or a quantity ( e.g., atomic_number(Aluminium, 13), inflation_rate(India, 10.9%)). This task presents peculiar challenges not found in standard IE, such as the difficulty of matching numbers in distant supervision and the importance of units. We design two extraction systems that require minimal human supervision per relation: (1) NumberRule, a rule based extractor, and (2) NumberTron, a probabilistic graphical model. We find that both systems dramatically outperform MultiR, a state-of-the-art non-numerical IE model, obtaining up to 25 points F-score improvement.

In this paper we propose a wrapper based PSO method for gene selection in microarray datasets, where we gradually refine the feature (gene) space from a very coarse level to a fine grained one, by reducing the gene set at each step of the algorithm. We use the linear support vector machine weight vector to serve as the initial gene pool selection. In addition, we also examine integration of other filter based ranking methods with our proposed approach. Experiments on publicly available datasets, Colon, Leukemia and T2D show that our approach selects only a very small subset of genes while yielding substantial improvements in accuracy over state-of-the-art evolutionary methods.

Many AI applications need to explicitly represent relational structure as well as handle uncertainty. First order probabilistic models combine the power of logic and probability to deal with such domains. A naive approach to inference in these models is to propositionalize the whole theory and carry out the inference on the ground network. Lifted inference techniques (such as lifted belief propagation; Singla and Domingos 2008) provide a more scalable approach to inference by combining together groups of objects which behave identically. In many cases, constructing the lifted network can itself be quite costly. In addition, the exact lifted network is often very close in size to the fully propositionalized model. To overcome these problems, we present approximate lifted inference, which groups together similar but distinguishable objects and treats them as if they were identical. Early stopping terminates the execution of the lifted network construction at an early stage resulting in a coarser network. Noise-tolerant hypercubes allow for marginal errors in the representation of the lifted network itself. Both of our algorithms can significantly speed up the process of lifted network construction as well as result in much smaller models. The coarseness of the approximation can be adjusted depending on the accuracy required, and we can bound the resulting error. Extensive evaluation on six domains demonstrates great efficiency gains with only minor (or no) loss in accuracy.

In present times, social forums such as Quora and Yahoo! Answers constitute powerful media through which people discuss on a variety of topics and express their intentions and thoughts. Here they often reveal their potential intent to purchase - 'Purchase Intent' (PI). A purchase intent is defined as a text expression showing a desire to purchase a product or a service in future. Extracting posts having PI from a user's social posts gives huge opportunities towards web personalization, targeted marketing and improving community observing systems. In this paper, we explore the novel problem of detecting PIs from social posts and classifying them. We find that using linguistic features along with statistical features of PI expressions achieves a significant improvement in PI classification over 'bag-of-words' based features used in many present day social-media classification tasks. Our approach takes into consideration the specifics of social posts like limited contextual information, incorrect grammar, language ambiguities, etc. by extracting features at two different levels of text granularity - word and phrase based features and grammatical dependency based features. Apart from these, the patterns observed in PI posts help us to identify some specific features.

In this paper, we shed light on how powerful congestion control based on local interactions may be obtained. We show how ants can use repellent pheromones and incorporate the effect of crowding to avoid traffic congestion on the optimal path. Based on these interactions, we propose an ant algorithm, the M-unit EigenAnt algorithm, that leads to the selection of the M shortest paths. The ratio of selection of each of these paths is also optimal and regulated by an optimal amount of pheromone on each of them. To the best of our knowledge, the M -unit EigenAnt algorithm is the first antalgorithm that explicitly ensures the selection of the M shortest paths and regulates the amount of pheromone on them such that it is asymptotically optimal. In fact, it is in contrast with most ant algorithms that aim to discover just a single best path. We provide its convergence analysis and show that the steady state distribution of pheromone aligns with the eigenvectors of the cost matrix, and thus is related to its measure of quality. We also provide analysis to show that this property ensues even when the food is moved or path lengths change during foraging. We show that this behavior is robust in the presence of fluctuations and quickly reflects the change in the M optimal solutions. This makes it suitable for not only distributed applications butalso dynamic ones as well. Finally, we provide simulation results for the convergence to the optimal solution under different initial biases, dynamism in lengths of paths, and discovery of new paths.