The Qualification Problem: A solution to the problem of anomalous models

AbstractIntelligent agents in open environments inevitably face the Qualification Problem: The executability of an action can never be predicted with absolute certainty; unexpected circumstances, albeit unlikely, may at any time prevent the successful performance of an action. Reasoning agents in real-world environments rely on a solution to the Qualification Problem in order to make useful predictions but also to explain and recover from unexpected action failures. Yet the main theoretical result known today in this context is a negative one: While a solution to the Qualification Problem requires to assume away by default abnormal qualifications of actions, straightforward minimization of abnormality falls prey to the production of anomalous models. We present an approach to the Qualification Problem which resolves this anomaly. Anomalous models are shown to arise from ignoring causality, and they are avoided by appealing to just this concept. Our theory builds on the established predicate logic formalism of the Fluent Calculus as a solution to the Frame Problem and to the Ramification Problem in reasoning about actions. The monotonic Fluent Calculus is enhanced by a default theory in order to obtain the nonmonotonic approach called for by the Qualification Problem. The approach has been implemented in an action programming language based on the Fluent Calculus and successfully applied to the high-level control of robots

ALPprolog --- A New Logic Programming Method for Dynamic Domains

Logic programming is a powerful paradigm for programming autonomous agents in dynamic domains, as witnessed by languages such as Golog and Flux. In this work we present ALPprolog, an expressive, yet efficient, logic programming language for the online control of agents that have to reason about incomplete information and sensing actions.Comment: 16 page

Under consideration for publication in Theory and Practice of Logic Programming ALPprolog -A New Logic Programming Method for Dynamic Domains

Abstract Logic programming is a powerful paradigm for programming autonomous agents in dynamic domains, as witnessed by languages such as Golog and Flux. In this work we present ALPprolog, an expressive, yet efficient, logic programming language for the online control of agents that have to reason about incomplete information and sensing actions

Online Learning and Planning in Cognitive Hierarchies

Complex robot behaviour typically requires the integration of multiple robotic and Artificial Intelligence (AI) techniques and components. Integrating such disparate components into a coherent system, while also ensuring global properties and behaviours, is a significant challenge for cognitive robotics. Using a formal framework to model the interactions between components can be an important step in dealing with this challenge. In this paper we extend an existing formal framework [Clark et al., 2016] to model complex integrated reasoning behaviours of robotic systems; from symbolic planning through to online learning of policies and transition systems. Furthermore the new framework allows for a more flexible modelling of the interactions between different reasoning components

Putting ABox Updates into Action

When trying to apply recently developed approaches for updating Description Logic ABoxes in the context of an action programming language, one encounters two problems. First, updates generate so-called Boolean ABoxes, which cannot be handled by traditional Description Logic reasoners. Second, iterated update operations result in very large Boolean ABoxes, which, however, contain a huge amount of redundant information. In this paper, we address both issues from a practical point of view

Ein contract-net-basiertes Lernverfahren für eine benutzeradaptive Interface-Agentur (Abstract)

Lenzmann B, Wachsmuth I. Ein contract-net-basiertes Lernverfahren für eine benutzeradaptive Interface-Agentur (Abstract). In: Thielscher M, Bornscheuer S-E, eds. Fortschritte der Künstlichen Intelligenz / Workshops KI-96. Dresden: Dresden University Press; 1996: 13

Spatiotemporal structure of intracranial electric fields induced by transcranial electric stimulation in humans and nonhuman primates

Transcranial electric stimulation (TES) is an emerging technique, developed to non-invasively modulate brain function. However, the spatiotemporal distribution of the intracranial electric fields induced by TES remains poorly understood. In particular, it is unclear how much current actually reaches the brain, and how it distributes across the brain. Lack of this basic information precludes a firm mechanistic understanding of TES effects. In this study we directly measure the spatial and temporal characteristics of the electric field generated by TES using stereotactic EEG (s-EEG) electrode arrays implanted in cebus monkeys and surgical epilepsy patients. We found a small frequency dependent decrease (10%) in magnitudes of TES induced potentials and negligible phase shifts over space. Electric field strengths were strongest in superficial brain regions with maximum values of about 0.5 mV/mm. Our results provide crucial information of the underlying biophysics in TES applications in humans and the optimization and design of TES stimulation protocols. In addition, our findings have broad implications concerning electric field propagation in non-invasive recording techniques such as EEG/MEG

The TMS Map Scales with Increased Stimulation Intensity and Muscle Activation

One way to study cortical organisation, or its reorganisation, is to use transcranial magnetic stimulation (TMS) to construct a map of corticospinal excitability. TMS maps are reported to be acquired with a wide variety of stimulation intensities and levels of muscle activation. Whilst MEPs are known to increase both with stimulation intensity and muscle activation, it remains to be established what the effect of these factors is on the map's centre of gravity (COG), area, volume and shape. Therefore, the objective of this study was to systematically examine the effect of stimulation intensity and muscle activation on these four key map outcome measures. In a first experiment, maps were acquired with a stimulation intensity of 110, 120 and 130% of resting threshold. In a second experiment, maps were acquired at rest and at 5, 10, 20 and 40% of maximum voluntary contraction. Map area and map volume increased with both stimulation intensity (P 0.09 in all cases). This result indicates the map simply scales with stimulation intensity and muscle activation

An overview and a roadmap for artificial intelligence in hematology and oncology.

Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals