Abduction is widely used in the task of plan recognition, since it can be viewed as the task of finding the best explanation for a set of observations. The major drawback of abduction is its computational complexity. The task of abductive reasoning quickly becomes intractable as the background knowledge is increased. Recent efforts in the field of computational linguistics have enriched computational resources for commonsense reasoning. The enriched knowledge base facilitates exploring practical plan recognition models in an open-domain. Therefore, it is essential to develop an efficient framework for such large-scale processing. In this paper, we propose an efficient implementation of Weighted abduction. Our framework transforms the problem of explanation finding in Weighted abduction into a linear programming problem. Our experiments showed that our approach efficiently solved problems of plan recognition and outperforms state-of-the-art tool for Weighted abduction.

This extended abstract describes a new platform for robotic manipulation research that was inspired by some of the first toys that human infants learn to manipulate. It summarizes the results of our existing research on pressing buttons and formulates some ideas for future work.

We present a framework that produces sentence-level summarizations of videos containing complex human activities that can be implemented as part of the Robot Perception Control Unit (RPCU). This is done via: 1) detection of pertinent objects in the scene: tools and direct-objects, 2) predicting actions guided by a large lexical corpus and 3) generating the most likely sentence description of the video given the detections. We pursue an active object detection approach by focusing on regions of high optical flow. Next, an iterative EM strategy, guided by language, is used to predict the possible actions. Finally, we model the sentence generation process as a HMM optimization problem, combining visual detections and a trained language model to produce a readable description of the video. Experimental results validate our approach and we discuss the implications of our approach to the RPCU in future applications.

We present a framework for semantic visual scene interpretation in a system with vision and language. In this framework the system consists of two modules, a language module and a vision module that communicate with each other in a form of a dialogue to actively interpret the scene. The language module is responsible for obtaining domain knowledge from linguistic resources and reasoning on the basis of this knowledge and the visual input. It iteratively creates questions that amount to an attention mechanism for the vision module which in turn shifts its focus to selected parts of the scene and applies selective segmentation and feature extraction. As a formalism for optimizing this dialogue we use information theory. We demonstrate the framework on the problem of recognizing a static scene from its objects and show preliminary results for the problem of human activity recognition from video. Experiments demonstrate the effectiveness of the active paradigm in introducing attention and additional constraints into the sensing process.

This paper reports on the linguistic analysis of a tag set of nearly 50,000 tags collected as part of the steve.museum project. The tags describe images of objects in museum collections. We present our results on morphological, part of speech and semantic analysis. We demonstrate that deeper tag processing provides valuable information for organizing and categorizing social tags. This promises to improve access to museum objects by leveraging the characteristics of tags and the relationships between them rather than treating them as individual items. The paper shows the value of using deep computational linguistic techniques in interdisciplinary projects on tagging over images of objects in museums and libraries. We compare our data and analysis to Flickr and other image tagging projects.

Being able to compactly represent large state spaces is crucial in solving a vast majority of practical stochastic planning problems. This requirement is even more stringent in the context of multi-agent systems, in which the world to be modeled also includes the mental state of other agents. This leads to a hierarchy of beliefs that results in a continuous, unbounded set of possible interactive states, as in the case of Interactive POMDPs. In this paper, we describe a novel representation for interactive belief hierarchies that combines first-order logic and probability. The semantics of this new formalism is based on recursively partitioning the belief space at each level of the hierarchy; in particular, the partitions of the belief simplex at one level constitute the vertices of the simplex at the next higher level. Since in general a set of probabilistic statements only partially specifies a probability distribution over the space of interest, we adopt the maximum entropy principle in order to convert it to a full specification.

Partially observable stochastic games (POSGs) are an attractive model for many multi-agent domains, but are computationally extremely difficult to solve. We present a new model, Markov games of incomplete information (MGII) which imposes a mild restriction on POSGs while overcoming their primary computational bottleneck. Finally we show how to convert a MGII into a continuous but bounded fully observable stochastic game. MGIIs represents the most general tractable model for multi-agent reinforcement learning to date.

Frequently, it is advantageous for an agent to model other agents in order to predict their behavior during an interaction. Modeling others as rational has a long tradition in AI and game theory, but modeling other agents’ departures from rationality is difficult and controversial. This paper proposes that bounded rationality be modeled as errors the agent being modeled is making while deciding on its action. We are motivated by the work on quantal response equilibria in behavioral game theory which uses Nash equilibria as the solution concept. In contrast, we use decision-theoretic maximization of expected utility. Quantal response assumes that a decision maker is rational, i.e., is maximizing his expected utility, but only approximately so, with an error rate characterized by a single error parameter. Another agent’s error rate may be unknown and needs to be estimated during an interaction. We show that the error rate of the quantal response can be estimated using Bayesian update of a suitable conjugate prior, and that it has a finitely dimensional sufficient statistic under strong simplifying assumptions. However, if the simplifying assumptions are relaxed, the quantal response does not admit a finite sufficient statistic and a more complex update is needed. This confirms the difficulty of using simple models of bounded rationality in general settings.

Having discovered a decision principle for a well-known prima facie duty theory in biomedical ethics to resolve particular cases of a common type of ethical dilemma, we developed three applications: a medical ethics advisor system, a medication reminder system and an instantiation of this system in a Nao robot. We are now developing a general, automated method for generating from scratch the ethics needed for a machine to function in a particular domain, without making the assumptions used in our prototype systems.

Mutual exclusions provide useful information for learn- ing classes of concepts. We designed MuSweeper as a MineSweeper-like game to collect mutual exclusions from web users. Using the mechanism of an exten- sive game with Imperfect information, our experiments showed MuSweeper to collect mutual exclusions with high precision and efficiency.