136 research outputs found
Meta-level argumentation framework for representing and reasoning about disagreement
The contribution of this thesis is to the field of Artificial Intelligence (AI), specifically
to the sub-field called knowledge engineering. Knowledge engineering involves the
computer representation and use of the knowledge and opinions of human experts.In real world controversies, disagreements can be treated as opportunities for
exploring the beliefs and reasoning of experts via a process called argumentation.
The central claim of this thesis is that a formal computer-based framework for
argumentation is a useful solution to the problem of representing and reasoning with
multiple conflicting viewpoints.The problem which this thesis addresses is how to represent arguments in domains in
which there is controversy and disagreement between many relevant points of view.
The reason that this is a problem is that most knowledge based systems are founded in
logics, such as first order predicate logic, in which inconsistencies must be eliminated
from a
theory in order for meaningful inference to be possible from it.I argue that it is possible to devise an argumentation framework by describing one
(FORA : Framework for Opposition and Reasoning about Arguments). FORA
contains a language for representing the views of multiple experts who disagree or
have differing opinions. FORA also contains a suite of software tools which can
facilitate debate, exploration of multiple viewpoints, and construction and revision of
knowledge bases which are challenged by opposing opinions or evidence.A fundamental part of this thesis is the claim that arguments are meta-level structures
which describe the relationships between statements contained in knowledge bases. It
is important to make a clear distinction between representations in knowledge bases
(the object-level) and representations of the arguments implicit in knowledge bases
(the meta-level). FORA has been developed to make this distinction clear and its main
benefit is that the argument representations are independent of the object-level
representation language. This is useful because it facilitates integration of arguments
from multiple sources using different representation languages, and because it enables
knowledge engineering decisions to be made about how to structure arguments and
chains of reasoning, independently of object-level representation decisions.I argue that abstract argument representations are useful because they can facilitate a
variety of knowledge engineering tasks. These include knowledge acquisition;
automatic abstraction from existing formal knowledge bases; and construction, rerepresentation,
evaluation and criticism of object-level knowledge bases. Examples
of software tools contained within FORA are used to illustrate these uses of
argumentation structures. The utility of a meta-level framework for argumentation,
and FORA in particular, is demonstrated in terms of an important real world
controversy concerning the health risks of a group of toxic compounds called
aflatoxins
Proceedings of the 11th Workshop on Nonmonotonic Reasoning
These are the proceedings of the 11th Nonmonotonic Reasoning Workshop. The aim of this series is to bring together active researchers in the broad area of nonmonotonic reasoning, including belief revision, reasoning about actions, planning, logic programming, argumentation, causality, probabilistic and possibilistic approaches to KR, and other related topics. As part of the program of the 11th workshop, we have assessed the status of the field and discussed issues such as: Significant recent achievements in the theory and automation of NMR; Critical short and long term goals for NMR; Emerging new research directions in NMR; Practical applications of NMR; Significance of NMR to knowledge representation and AI in general
Proceedings of the Sixth NASA Langley Formal Methods (LFM) Workshop
Today's verification techniques are hard-pressed to scale with the ever-increasing complexity of safety critical systems. Within the field of aeronautics alone, we find the need for verification of algorithms for separation assurance, air traffic control, auto-pilot, Unmanned Aerial Vehicles (UAVs), adaptive avionics, automated decision authority, and much more. Recent advances in formal methods have made verifying more of these problems realistic. Thus we need to continually re-assess what we can solve now and identify the next barriers to overcome. Only through an exchange of ideas between theoreticians and practitioners from academia to industry can we extend formal methods for the verification of ever more challenging problem domains. This volume contains the extended abstracts of the talks presented at LFM 2008: The Sixth NASA Langley Formal Methods Workshop held on April 30 - May 2, 2008 in Newport News, Virginia, USA. The topics of interest that were listed in the call for abstracts were: advances in formal verification techniques; formal models of distributed computing; planning and scheduling; automated air traffic management; fault tolerance; hybrid systems/hybrid automata; embedded systems; safety critical applications; safety cases; accident/safety analysis
Computational Complexity of Strong Admissibility for Abstract Dialectical Frameworks
Abstract dialectical frameworks (ADFs) have been introduced as a formalism for modeling and evaluating argumentation allowing general logical satisfaction conditions. Different criteria used to settle the acceptance of arguments arecalled semantics. Semantics of ADFs have so far mainly been defined based on the concept of admissibility. Recently, the notion of strong admissibility has been introduced for ADFs. In the current work we study the computational complexityof the following reasoning tasks under strong admissibility semantics. We address 1. the credulous/skeptical decision problem; 2. the verification problem; 3. the strong justification problem; and 4. the problem of finding a smallest witness of strong justification of a queried argument
Rational Agents: Prioritized Goals, Goal Dynamics, and Agent Programming Languages with Declarative Goals
I introduce a specification language for modeling an agent's prioritized goals and their dynamics. I use the situation calculus along with Reiter's solution to the frame problem and predicates for describing agents' knowledge as my base formalism. I further enhance this language by introducing a new sort of infinite paths. Within this language, I discuss how to systematically specify prioritized goals and how to precisely describe the effects of actions on these goals. These actions include adoption and dropping of goals and subgoals. In this framework, an agent's intentions are formally specified as the prioritized intersection of her goals. The ``prioritized'' qualifier above means that the specification must respect the priority ordering of goals when choosing between two incompatible goals. I ensure that the agent's intentions are always consistent with each other and with her knowledge. I investigate two variants with different commitment strategies. Agents specified using the ``optimizing'' agent framework always try to optimize their intentions, while those specified in the ``committed'' agent framework will stick to their intentions even if opportunities to commit to higher priority goals arise when these goals are incompatible with their current intentions. For these, I study properties of prioritized goals and goal change. I also give a definition of subgoals, and prove properties about the goal-subgoal relationship.
As an application, I develop a model for a Simple Rational Agent Programming Language (SR-APL) with declarative goals. SR-APL is based on the ``committed agent'' variant of this rich theory, and combines elements from Belief-Desire-Intention (BDI) APLs and the situation calculus based ConGolog APL. Thus SR-APL supports prioritized goals and is grounded on a formal theory of goal change. It ensures that the agent's declarative goals and adopted plans are consistent with each other and with her knowledge. In doing this, I try to bridge the gap between agent theories and practical agent programming languages by providing a model and specification of an idealized BDI agent whose behavior is closer to what a rational agent does. I show that agents programmed in SR-APL satisfy some key rationality requirements
Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective
Data-driven decision making is becoming an integral part of manufacturing
companies. Data is collected and commonly used to improve efficiency and
produce high quality items for the customers. IoT-based and other forms of
object tracking are an emerging tool for collecting movement data of
objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over
space and time. Movement data can provide valuable insights like process
bottlenecks, resource utilization, effective working time etc. that can be used
for decision making and improving efficiency.
Turning movement data into valuable information for industrial management and
decision making requires analysis methods. We refer to this process as movement
analytics. The purpose of this document is to review the current state of work
for movement analytics both in manufacturing and more broadly.
We survey relevant work from both a theoretical perspective and an
application perspective. From the theoretical perspective, we put an emphasis
on useful methods from two research areas: machine learning, and logic-based
knowledge representation. We also review their combinations in view of movement
analytics, and we discuss promising areas for future development and
application. Furthermore, we touch on constraint optimization.
From an application perspective, we review applications of these methods to
movement analytics in a general sense and across various industries. We also
describe currently available commercial off-the-shelf products for tracking in
manufacturing, and we overview main concepts of digital twins and their
applications
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