Using temporal abduction for biosignal interpretation: A case study on QRS detection

In this work, we propose an abductive framework for biosignal interpretation, based on the concept of Temporal Abstraction Patterns. A temporal abstraction pattern defines an abstraction relation between an observation hypothesis and a set of observations constituting its evidence support. New observations are generated abductively from any subset of the evidence of a pattern, building an abstraction hierarchy of observations in which higher levels contain those observations with greater interpretative value of the physiological processes underlying a given signal. Non-monotonic reasoning techniques have been applied to this model in order to find the best interpretation of a set of initial observations, permitting even to correct these observations by removing, adding or modifying them in order to make them consistent with the available domain knowledge. Some preliminary experiments have been conducted to apply this framework to a well known and bounded problem: the QRS detection on ECG signals. The objective is not to provide a new better QRS detector, but to test the validity of an abductive paradigm. These experiments show that a knowledge base comprising just a few very simple rhythm abstraction patterns can enhance the results of a state of the art algorithm by significantly improving its detection F1-score, besides proving the ability of the abductive framework to correct both sensitivity and specificity failures.Comment: 7 pages, Healthcare Informatics (ICHI), 2014 IEEE International Conference o

A Pragmatic Reading of Friedman's Methodological Essay and What It Tells Us for the Discussion of ABMs

The issues of empirical calibration of parameter values and functional relationships describing the interactions between the various actors plays an important role in agent based modelling. Agent-based models range from purely theoretical exercises focussing on the patterns in the dynamics of interactions processes to modelling frameworks which are oriented closely at the replication of empirical cases. ABMs are classified in terms of their generality and their use of empirical data. In the literature the recommendation can be found to aim at maximizing both criteria by building so-called 'abductive models'. This is almost the direct opposite of Milton Friedman's famous and provocative methodological credo 'the more significant a theory, the more unrealistic the assumptions'. Most methodologists and philosophers of science have harshly criticised Friedman's essay as inconsistent, wrong and misleading. By presenting arguments for a pragmatic reinterpretation of Friedman's essay, we will show why most of the philosophical critique misses the point. We claim that good simulations have to rely on assumptions, which are adequate for the purpose in hand and those are not necessarily the descriptively accurate ones.Methodology, Agent-Based Modelling, Assumptions, Calibration

Grounding Dynamic Spatial Relations for Embodied (Robot) Interaction

This paper presents a computational model of the processing of dynamic spatial relations occurring in an embodied robotic interaction setup. A complete system is introduced that allows autonomous robots to produce and interpret dynamic spatial phrases (in English) given an environment of moving objects. The model unites two separate research strands: computational cognitive semantics and on commonsense spatial representation and reasoning. The model for the first time demonstrates an integration of these different strands.Comment: in: Pham, D.-N. and Park, S.-B., editors, PRICAI 2014: Trends in Artificial Intelligence, volume 8862 of Lecture Notes in Computer Science, pages 958-971. Springe

A comparative survey of integrated learning systems

This paper presents the duction framework for unifying the three basic forms of inference - deduction, abduction, and induction - by specifying the possible relationships and influences among them in the context of integrated learning. Special assumptive forms of inference are defined that extend the use of these inference methods, and the properties of these forms are explored. A comparison to a related inference-based learning frame work is made. Finally several existing integrated learning programs are examined in the perspective of the duction framework

Multi-agent planning using an abductive : event calculus

Temporal reasoning within distributed Artificial Intelligence Systems is faced with the problem of concurrent streams of action. Well known, logic-based systems using the SITUATION CALCULUS solve the frame problem in a purely linear manner. Recent research, however, has revealed that the EVENT CALCULUS under the abduction principle is capable of nonlinear planning. In this report, we present a planning service module which incorporates this approach into a constraint logic framework and even allows a notion of strong nonlinearity. The work includes the axiomatisation of appropriate versions of the EVENT CALCULUS, the development of a suitably sound and complete proof procedure that supports abduction and the implementation of both of these layers on the constraint platform OZ. We demonstrate prototypically how this module, EVE, can be integrated into an existing multi-agent architecture and evaluate the behaviour of such agents within an application domain, the loading dock scenario

Approximations in diagnosis: motivations and techniques

We argue that diagnosis should not be seen as solving a problem with a unique definition, but rather that there exists a whole space of reasonable notions of diagnosis. These notions can be seen as mutual approximations. We present a number of reasons for choosing among different notions of diagnosis. We also present an exhaustive categorisation of techniques that can be employed to obtain approximations, as well as a number of specific example techniques for each category. We also show that it is possible to characterise the relations between the approximations obtained by these techniques