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.

GENE_ARCH is a Generative Design System that combines Pareto Genetic Algorithms with an advanced energy simulation engine. This work explores its integration with a Shape Grammar, acting as GENE_ARCH’s shape generation module. The islamic patio house typology is readdressed in a contemporary context, by improving its energy-efficiency, and rethinking its role in the genesis of high-density urban areas, while respecting its specific spatial organization and cultural grounding. Field work was carried out in Marrakesh, surveying a number of patio houses, becoming the Corpus of Design, from where a shape grammar was generated. The computational implementation of the patio house grammar was done within GENE_ARCH. The resulting program was able to generate new, alternative patio houses designs that were more energy efficient, while respecting the traditional rules captured from the analysis of existing houses. After the computational system was fully implemented, it was possible to realise a large number of experiments. The first experiments kept more restrained rules, thus generating new designs that closer resembled the existing ones. The progressive relaxation of rules and constraints allowed for a larger number of variations to emerge. Analysis of energy results provide insight into the main patterns resulting from the GA search processes.

It has been argued that causal rules are necessary for representing both implicit side-effects of actions and action qualifications, and there have been a number different approaches for representing causal rules in the area of formal theories of actions. These different approaches in general agree on rules without cycles. However, they differ on causal rules with mutual cyclic dependencies, both in terms of how these rules are supposed to be represented and their semantics. In this paper we show that by adding one more minimization to Lin's circumscriptive causal theory in the situation calculus, we can have a uniform representation of causal rules including those with cyclic dependencies. We also demonstrate that sometimes causal rules can be compiled into logically equivalent (under a proposed semantics) successor state axioms even in the presence of cyclical dependencies between fluents.

While computers assist humans with tasks such as navigation that involve spatial aspects, agents that can interact in a meaningful way in this context are still in their infancy. One core issue is the mismatch in the representation of spatial information a computer-based system is likely to use, and the one a human is likely to use. Computers are better suited for quantitative schemes such as maps or diagrams that rely on measurable distances between entities. Humans frequently use higher-level, domain-specific conceptual representations such as buildings, rooms, or streets for orientation purposes. Combined with the person-centric world view that we often assume when we refer to spatial information, it is challenging for agents to convert statements using spatial references into assertions that match their own internal representation. In this paper, we discuss an approach that uses natural language processing and information extraction tool kits to identify entities and statements about their spatial relations. These extractions are then processed by a spatial reasoner to convert them from the human conceptual space into the quantitative space used by the computer-based agent.

We provide reformulations and generalizations of both the semantics of logic programs by Faber, Leone and Pfeifer and its extension to arbitrary propositional formulas by Truszczynski. Unlike the previous definitions, our generalizations refer neither to grounding nor to fixpoints, and apply to first-order formulas containing aggregate expressions. Similar to the first-order stable model semantics by Ferraris, Lee and Lifschitz, the reformulations presented here are based on syntactic transformations that are similar to circumscription. The reformulations provide useful insights into the FLP semantics and its relationship to circumscription and the first-order stable model semantics.

Humans naturally use multiple modes of instruction while teaching one another. We would like our robots and artificial agents to be instructed in the same way, rather than programmed. In this paper, we review prior work on human instruction of autonomous agents and present observations from two exploratory pilot studies and the results of a full study investigating how multiple instruction modes are used by humans. We describe our Bootstrapped Learning User Interface, a prototype multiinstruction interface informed by our human-user studies.

It has been demonstrated that people have a tendency to adapt both their linguistic representations and physical Communication is often required between agents as they actions in response to those they are interacting with, i.e., attempt to solve collaborative multi-agent tasks. This is they tend to formulate behavior and speech that will be particularly true in conditions in which an agent is working salient and sensible to a collaborating partner (Whittaker alongside a human--clearly, conventional electronic 2003). Collaboration in humans occurs via a process in communication is not feasible in this scenario; rather, these which people align their linguistic representations of the agents, including humans, must take advantage of physical environment allowing for more effective communicative communication in the shared context to confer necessary behavior. This alignment is achieved via a process in information. As an agent observes the actions of the others, which local alignment of environmental representations, it must modify its own behavior accordingly.

Trust is an approach to managing the uncertainty about autonomous entities and the information they store, and so can play an important role in any decentralized system. As a result, trust has been widely studied in multiagent systems and related fields such as the semantic web. Here we introduce a simple approach to reasoning about trust with logi

The collaboration between a human and a robot is here understood as a learning process mediated by the instructor prompt behaviours and the apprentice collecting information from them to learn a plan. The instructor wears the Gaze Machine, a wearable device gathering and conveying visual and audio input from the instructor while executing a task. The robot, on the other hand, is eager to learn both the best sequence of actions, their timing and how they interlace. The cross relation among actions is specified both in terms of time intervals for their execution, and in terms of location in space to cope with the instruction interaction with people and objects in the scene. We outline this process: how to transform the rich information delivered by the Gaze Machine into a plan. Specifically, how to obtain a map of the instructor positions and his gaze position, via visual slam and gaze fixations; further, how to obtain an action map from the running commentaries and the topological maps and, finally, how to obtain a temporal net of the relevant actions that have been extracted. The learned structure is then managed by the flexible time paradigm of flexible planning in the Situation Calculus for execution monitoring and plan generation.