This paper addresses one of the key components of the mining process: the geological prediction of natural resources from spatially distributed measurements. We present a novel approach combining undirected graphical models with ensemble classifiers to provide 3D geological models from multiple sensors installed in an autonomous drill rig. Drill sensor measurements used for drilling automation, known as measurement-while-drilling (MWD) data, have the potential to provide an estimate of the geological properties of the rocks being drilled. The proposed method maps MWD parameters to rock types while considering spatial relationships, i.e., associating measurements obtained from neighboring regions. We use a conditional random field with local information provided by boosted decision trees to jointly reason about the rock categories of neighboring measurements. To validate the approach, MWD data was collected from a drill rig operating at an iron ore mine. Graphical models of the 3D structure present in real data sets possess a high number of nodes, edges and cycles, making them intractable for exact inference. We provide a comparison of three approximate inference methods to calculate the most probable distribution of class labels. The empirical results demonstrate the benefits of spatial modeling through graphical models to improve classification performance.

In a seminal paper, Lin and Reiter introduced the notion of progression for basic action theories in the situation calculus. Earlier works by Moore, Scherl and Levesque extended the situation calculus to account for knowledge. In this paper, we study progression of knowledge in the situation calculus. We first adapt the concept of bisimulation from modal logic and extend Lin and Reiter's notion of progression to accommodate knowledge. We show that for physical actions, progression of knowledge reduces to forgetting predicates in first-order modal logic. We identify a class of first-order modal formulas for which forgetting an atom is definable in first-order modal logic. This class of formulas goes beyond formulas without quantifying-in. We also identify a simple case where forgetting a predicate reduces to forgetting a finite number of atoms. Thus we are able to show that for local-effect physical actions, when the initial KB is a formula in this class, progression of knowledge is definable in first-order modal logic. Finally, we extend our results to the multi-agent case.

Heuristics used for solving hard real-time search problems have regions with depressions. Such regions are bounded areas of the search space in which the heuristic function is exceedingly low compared to the actual cost to reach a solution. Real-time search algorithms easily become trapped in those regions since the heuristic values of states in them may need to be updated multiple times, which results in costly solutions. State-of-the-art real-time search algorithms like LSS-LRTA*, LRTA*(k), etc., improve LRTA*'s mechanism to update the heuristic, resulting in improved performance. Those algorithms, however, do not guide search towards avoiding or escaping depressed regions. This paper presents depression avoidance, a simple real-time search principle to guide search towards avoiding states that have been marked as part of a heuristic depression. We apply the principle to LSS-LRTA* producing aLSS-LRTA*, a new real-time search algorithm whose search is guided towards exiting regions with heuristic depressions. We show our algorithm outperforms LSS-LRTA* in standard real-time benchmarks. In addition we prove aLSS-LRTA* has most of the good theoretical properties of LSS-LRTA*.

Classical planning has been notably successful in synthesizing finite plans to achieve states where propositional goals hold. In the last few years, classical planning has also been extended to incorporate temporally extended goals, expressed in temporal logics such as LTL, to impose restrictions on the state sequences generated by finite plans. In this work, we take the next step and consider the computation of infinite plans for achieving arbitrary LTL goals. We show that infinite plans can also be obtained efficiently by calling a classical planner once over a classical planning encoding that represents and extends the composition of the planning domain and the Buchi automaton representing the goal. This compilation scheme has been implemented and a number of experiments are reported.

We present the notion of Social Instruments as mechanisms that facilitate the emergence of conventions from repeated interactions between members of a society. Specifically, we focus on two social instruments: rewiring and observation. Our main goal is to provide agents with tools that allow them to leverage their social network of interactions when effectively addressing coordination and learning problems, paying special attention to dissolving meta-stable subconventions. Initial experiments throw some light on how Self-Reinforcing Substructures (SRS) in the network prevent full convergence, resulting in reduced convergence rates. The use of an effective composed social instrument (observation + rewiring) allow agents to eliminate the subconventions that otherwise remained meta-stable.

A fundamental step in sentence comprehension involves assigning semantic roles to sentence constituents. To accomplish this, the listener must parse the sentence, find constituents that are candidate arguments, and assign semantic roles to those constituents. Where do children learning their first languages begin in solving this problem? Even assuming children can derive a rough meaning for the sentence from the situation, how do they begin to map this meaning to the structure and the structure to the form of the sentence? In this paper we use feedback from a semantic role labeling (SRL) task to improve the intermediate syntactic representations that feed the SRL. We accomplish this by training an intermediate classifier using signals derived from latent structure optimization techniques. By using a separate classifier to predict internal structure we see benefits due to knowledge embedded in the classifier's feature representation. This extra structure allows the system to begin to learn using weaker, more plausible semantic feedback.

In this paper we present a novel user interface that integrates two popular approaches to language translation for travelers allowing multimodal communication between the parties involved: the picture-book, in which the user simply points to multiple picture icons representing what they want to say, and the statistical machine translation system that can translate arbitrary word sequences. Our prototype system tightly couples both processes within a translation framework that inherits many of the the positive features of both approaches, while at the same time mitigating their main weaknesses. Our system differs from traditional approaches in that its mode of input is a sequence of pictures, rather than text or speech. Text in the source language is generated automatically, and is used as a detailed representation of the intended meaning. The picture sequence which not only provides a rapid method to communicate basic concepts but also gives a `second opinion' on the machine transition output that catches machine translation errors and allows the users to retry the translation, avoiding misunderstandings.

In our first experiment, we show that using A human's ability to diagnose errors, gather data, multiple models to identify potential label noise can provide and generate features in order to build better a threefold reduction in the number of spurious examples a models is largely untapped. We hypothesize that practitioner examines. In our second experiment, we show analyzing results from multiple models can help that analyses of multiple models can identify examples that people diagnose errors by understanding are significantly more likely to respond to additional relationships among data, features, and algorithms.

In real-world applications, the effective integration of learning and reasoning in a cognitive agent model is a difficult task. However, such integration may lead to a better understanding, use and construction of more realistic models. Unfortunately, existing models are either oversimplified or require much processing time, which is unsuitable for online learning and reasoning. Currently, controlled environments like training simulators do not effectively integrate learning and reasoning. In particular, higher-order concepts and cognitive abilities have many unknown temporal relations with the data, making it impossible to represent such relationships by hand. We introduce a novel cognitive agent model and architecture for online learning and reasoning that seeks to effectively represent, learn and reason in complex training environments. The agent architecture of the model combines neural learning with symbolic knowledge representation. It is capable of learning new hypotheses from observed data, and infer new beliefs based on these hypotheses. Furthermore, it deals with uncertainty and errors in the data using a Bayesian inference model. The validation of the model on real-time simulations and the results presented here indicate the promise of the approach when performing online learning and reasoning in real-world scenarios, with possible applications in a range of areas.

Typical solutions for agents assessing trust relies on the circulation of information on the individual level, i.e. reputational images, subjective experi- ences, statistical analysis, etc. This work presents an alternative approach, inspired to the cognitive heuristics enabling humans to reason at a categorial level. The approach is envisaged as a crucial ability for agents in order to: (1) estimate trustworthiness of unknown trustees based on an ascribed mem- bership to categories; (2) learn a series of emer- gent relations between trustees observable proper- ties and their effective abilities to fulfill tasks in sit- uated conditions. On such a basis, categorization is provided to recognize signs (Manifesta) through which hidden capabilities (Kripta) can be inferred. Learning is provided to refine reasoning attitudes needed to ascribe tasks to categories. A series of ar- chitectures combining categorization abilities, indi- vidual experiences and context awareness are eval- uated and compared in simulated experiments.