Granularity is the concept of breaking down an event into smaller parts or granules such that each individual granule plays a part in the higher level event. Humans can seamlessly shift their granularity perspectives while reading or understanding a text. To emulate such a mechanism, we describe a theory for inferring this information automatically from raw input text descriptions and some background knowledge to learn the global behavior of event descriptions from local behavior of components. We also elaborate on the importance of discovering granularity structures for solving NLP problems such as — automated question answering and text summarization.

Decision-making is crucially important at all levels of biological complexity, from within single-celled organisms, through neural populations within the vertebrate brain, to collections of social organisms such as colonies of ants and honeybees, or societies of humans. What are the prospects for unifying the study of these apparently disparate systems? All can be conceptualised as voting systems at the appropriate level. In this review I will argue that optimality theory can be of fundamental importance in understanding all these systems. In particular I will argue that for groups without conflict of interests, such as neurons and social insect colonies, similar mechanisms could implement statistically optimal decision-making in apparently highly different systems at different levels of biological complexity. I will consider what currency these systems should optimize, and speculate about the possible application of this understanding to the design of voting systems where individual group members' interests are aligned, such as in certain types of human group, and in collectives of robots. I will also consider how established results from economics and political science, notably Arrow's Impossibility Theorem and Condorcet’s ‘jury theorem’, might relate to what we know of social insect voting systems, where interesting effects such as the emergence of collective rationality from the voting of irrational individuals have recently been demonstrated.

In this work, we present a causal extension of logic programming under the stable models semantics where, for a given stable model, we capture the alternative causes of each true atom. The syntax is extended by the simple addition of an optional reference label per each rule in the program. Then, the obtained causes rely on the concept of a causal proof: an inverted tree of labels that keeps track of the ordered application of rules that has allowed deriving a given true atom.

One of the challenges in accurately applying metrics for life cycle assessment lies in accounting for both irreducible and inherent uncertainties in how a design will perform under real world conditions. This paper presents a preliminary study that compares two strategies, one simulation-based and one set-based, for propagating uncertainty in a system. These strategies for uncertainty propagation are then aggregated. This work is conducted in the context of an amorphous photovoltaic (PV) panel, using data gathered from the National Solar Radiation Database, as well as realistic data collected from an experimental hardware setup specifically for this study. Results show that the influence of various sources of uncertainty can vary widely, and in particular that solar radiation intensity is a more significant source of uncertainty than the efficiency of a PV panel. This work also shows both set-based and simulation-based approaches have limitations and must be applied thoughtfully to prevent unrealistic results. Finally, it was found that aggregation of the two uncertainty propagation methods provided faster results than either method alone.

Organizational agility is a key challenge in today's business world. The Knowledge-Intensive Service Support approach tackles agility by combining process modeling and business rules. In the paper at hand, we present five approaches of process mining that could further increase the agility of processes by improving an existing process model.

Commonsense is a challenge not only for representation and reasoning but also for large scale knowledge engineering required to capture the breadth of our "everyday" world. One approach to knowledge engineering is to "outsource" the effort to the public through games that generate structured commonsense knowledge from user play. To date, such games have focused on symbolic and textual knowledge. However, an effective commonsense reasoning system will require spatial and physical reasoning capabilities. In this paper, I propose a tool for gathering commonsense information from ordinary people. It is a user-friendly 3D sculpting tool for modeling and annotating models of physical objects and spaces.

While much health data is available online, patients who are not technically astute may be unable to access it because they may not know the relevant resources, they may be reluctant to confront an unfamiliar interface, and they may not know how to compose an answer from information provided by multiple heterogeneous resources. We describe ongoing research in using natural English text queries and automated deduction to obtain answers based on multiple structured data sources in a specific subject domain. Each English query is transformed using natural language technology into an unambiguous logical form; this is submitted to a theorem prover that operates over an axiomatic theory of the subject domain. Symbols in the theory are linked to relations in external databases known to the system. An answer is obtained from the proof, along with an English language explanation of how the answer was obtained. Answers need not be present explicitly in any of the databases, but rather may be deduced or computed from the information they provide. Although English is highly ambiguous, the natural language technology is informed by subject domain knowledge, so that readings of the query that are syntactically plausible but semantically impossible are discarded. When a question is still ambiguous, the system can interrogate the patient to determine what meaning was intended. Additional queries can clarify earlier ones or ask questions referring to previously computed answers. We describe a prototype system, Quadri, which answers questions about HIV treatment using the Stanford HIV Drug Resistance Database and other resources. Natural language processing is provided by PARC’s Bridge, and the deductive mechanism is SRI’s SNARK theorem prover. We discuss some of the problems that must be faced to make this approach work, and some of our solutions.

This paper presents the formalization of a biological process, cell division, in modular action language ALM. We show how the features of ALM — modularity, separation between an uninterpreted theory and its interpretation — lead to a simple and elegant solution that can be used in answering questions from biology textbooks.

In this paper, we review the paradigm of inductive process modeling and examine its application to human physiology. This framework represents models as a set of interacting processes, each with associated differential or alegraic equations that express causal relations among variables. Simulating such a quantitative process model produces trajectories for variables over time that one can compare to observations. Background knowledge about candidate processes lets one carry out search through the space of model structures and their associated parameters, and thus identify quantitative models that explain time-series data. We present an initial process model for aspects of human physiology, consider its uses for health monitoring, and discuss the induction of such models. In closing, we discuss related efforts on physiological modeling and our plans for collecting data to evaluate our framework in this domain.

We present a framework to represent and reason about narratives that combines logical and probabilistic representations of commonsense knowledge. Unlike most natural language understanding systems, which merely extract facts or semantic roles, our system builds probabilistic representations of the temporal sequence of world states and actions implied by a narrative. We use probabilistic actions to represent ambiguities and uncertainties in the narrative. We present algorithms that take a representation of a narrative, derive all possible interpretations of the narrative, and answer probabilistic queries by marginalizing over all the interpretations. With a focus on spatial contexts, we demonstrate our framework on an example narrative. To this end, we apply natural language pro- cessing (NLP) tools together with statistical approaches over common sense knowledge bases.