678 research outputs found
Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems
The modelling, analysis, and visualisation of dynamic geospatial phenomena
has been identified as a key developmental challenge for next-generation
Geographic Information Systems (GIS). In this context, the envisaged
paradigmatic extensions to contemporary foundational GIS technology raises
fundamental questions concerning the ontological, formal representational, and
(analytical) computational methods that would underlie their spatial
information theoretic underpinnings.
We present the conceptual overview and architecture for the development of
high-level semantic and qualitative analytical capabilities for dynamic
geospatial domains. Building on formal methods in the areas of commonsense
reasoning, qualitative reasoning, spatial and temporal representation and
reasoning, reasoning about actions and change, and computational models of
narrative, we identify concrete theoretical and practical challenges that
accrue in the context of formal reasoning about `space, events, actions, and
change'. With this as a basis, and within the backdrop of an illustrated
scenario involving the spatio-temporal dynamics of urban narratives, we address
specific problems and solutions techniques chiefly involving `qualitative
abstraction', `data integration and spatial consistency', and `practical
geospatial abduction'. From a broad topical viewpoint, we propose that
next-generation dynamic GIS technology demands a transdisciplinary scientific
perspective that brings together Geography, Artificial Intelligence, and
Cognitive Science.
Keywords: artificial intelligence; cognitive systems; human-computer
interaction; geographic information systems; spatio-temporal dynamics;
computational models of narrative; geospatial analysis; geospatial modelling;
ontology; qualitative spatial modelling and reasoning; spatial assistance
systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964);
Special Issue on: Geospatial Monitoring and Modelling of Environmental
Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press
A first-order Temporal Logic for Actions
We present a multi-modal action logic with first-order modalities, which
contain terms which can be unified with the terms inside the subsequent
formulas and which can be quantified. This makes it possible to handle
simultaneously time and states. We discuss applications of this language to
action theory where it is possible to express many temporal aspects of actions,
as for example, beginning, end, time points, delayed preconditions and results,
duration and many others. We present tableaux rules for a decidable fragment of
this logic
Narrative based Postdictive Reasoning for Cognitive Robotics
Making sense of incomplete and conflicting narrative knowledge in the
presence of abnormalities, unobservable processes, and other real world
considerations is a challenge and crucial requirement for cognitive robotics
systems. An added challenge, even when suitably specialised action languages
and reasoning systems exist, is practical integration and application within
large-scale robot control frameworks.
In the backdrop of an autonomous wheelchair robot control task, we report on
application-driven work to realise postdiction triggered abnormality detection
and re-planning for real-time robot control: (a) Narrative-based knowledge
about the environment is obtained via a larger smart environment framework; and
(b) abnormalities are postdicted from stable-models of an answer-set program
corresponding to the robot's epistemic model. The overall reasoning is
performed in the context of an approximate epistemic action theory based
planner implemented via a translation to answer-set programming.Comment: Commonsense Reasoning Symposium, Ayia Napa, Cyprus, 201
Cognitive Interpretation of Everyday Activities - Toward Perceptual Narrative Based Visuo-Spatial Scene Interpretation
We position a narrative-centred computational model for high-level knowledge representation and reasoning in the context of a range of assistive technologies concerned with visuo-spatial perception and cognition tasks. Our proposed narrative model encompasses aspects such as space, events, actions, change, and interaction from the viewpoint of commonsense reasoning and learning in large-scale cognitive systems. The broad focus of this paper is on the domain of human-activity interpretation in smart environments, ambient intelligence etc. In the backdrop of a smart meeting cinematography domain, we position the proposed narrative model, preliminary work on perceptual narrativisation, and the immediate outlook on constructing general-purpose open-source tools for perceptual narrativisation
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A general approach to temporal reasoning about action and change
Reasoning about actions and change based on common sense knowledge is one of the most important and difficult tasks in the artificial intelligence research area. A series of such tasks are identified which motivate the consideration and application of reasoning formalisms. There follows a discussion of the broad issues involved in modelling time and constructing a logical language. In general, worlds change over time. To model the dynamic world, the ability to predict what the state of the world will be after the execution of a particular sequence of actions, which take time and to explain how some given state change came about, i.e. the causality are basic requirements of any autonomous rational agent.
The research work presented herein addresses some of the fundamental concepts and the relative issues in formal reasoning about actions and change. In this thesis, we employ a new time structure, which helps to deal with the so-called intermingling problem and the dividing instant problem. Also, the issue of how to treat the relationship between a time duration and its relative time entity is examined. In addition, some key terms for representing and reasoning about actions and change, such as states, situations, actions and events are formulated. Furthermore, a new formalism for reasoning about change over time is presented. It allows more flexible temporal causal relationships than do other formalisms for reasoning about causal change, such as the situation calculus and the event calculus. It includes effects that start during, immediately after, or some time after their causes, and which end before, simultaneously with, or after their causes. The presented formalism allows the expression of common-sense causal laws at high level. Also, it is shown how these laws can be used to deduce state change over time at low level. Finally, we show that the approach provided here is expressive
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Regulating competence-based access to agent societies
Advances in ubiquitous computing have resulted in changes to the way we access and use everyday applications, e.g. reading mail and booking tickets. At the same time, users interact with these applications in a variety of ways, each with different characteristics, e.g., different degrees of bandwidth, different payment schemes supported and so on. These are highly dynamic interactions, as some of the applications might become unavailable (either temporarily or permanently) or their behaviour may change. As the user has to deal with a large number of proactive and dynamic applications every day, he will need a personal assistant that possesses similar characteristics. The agent paradigm meets this requirement, since it exhibits the necessary features. As a result, the user will provide its personal agent assistant with a goal, e.g. I need a smartphone which costs less than three hundred pounds, and the agent will have to use a number of applications offering information on smartphones so that it finds the requested one. This, in turn, raises a number of issues regarding the organisation and the degrees of access to these services as well as the correctness of their descriptions.
In this work, we propose the organisation of applications around the concept of artificial agent societies, to which access would be possible only by a positive evaluation of an agent's application. The agent will provide the Authority Agent with the role it is applying for and its competencies in the context of a protocol, i.e., the messages that it can utter/understand. The Authority Agent will then check to see if the applicant agent is a competent user of the protocols; if yes, entry is granted.
Assuming that access is granted, the next issue is to decide on the protocol(s) that agent receives. As providing the full protocol will cause security and overload problems, we only need to provide the part required for the agent to play its role. We show how this can be done and how we can repair certain protocols so that they are indeed enactable once this role decomposition is performed
Pictorial Narratives and Temporal Refinement
Refinements are proposed to the default reading of two successive pictures p,p' as p and then p'. The refinements are based on the Aristotelian dictum, no time without change, and the principle of inertia, no change without force, guided by the adage, a picture's worth a thousand words. Words describing pictures are formalized as predicates, some stative and some non-stative (expressing forces), and interpreted (in either case) over strings qua models, subject to finite-state projections supporting variable granularity. A form of string iconicity is explored, with an eye to more transparent representations
Learning relational event models from video
Event models obtained automatically from video can be used in applications ranging from abnormal event detection to content based video retrieval. When multiple agents are involved in the events, characterizing events naturally suggests encoding interactions as relations. Learning event models from this kind of relational spatio-temporal data using relational learning techniques such as Inductive Logic Programming (ILP) hold promise, but have not been successfully applied to very large datasets which result from video data. In this paper, we present a novel framework REMIND (Relational Event Model INDuction) for supervised relational learning of event models from large video datasets using ILP. Efficiency is achieved through the learning from interpretations setting and using a typing system that exploits the type hierarchy of objects in a domain. The use of types also helps prevent over generalization. Furthermore, we also present a type-refining operator and prove that it is optimal. The learned models can be used for recognizing events from previously unseen videos. We also present an extension to the framework by integrating an abduction step that improves the learning performance when there is noise in the input data. The experimental results on several hours of video data from two challenging real world domains (an airport domain and a physical action verbs domain) suggest that the techniques are suitable to real world scenarios
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