A Methodology for Modeling Expert Knowledge for Development of Agent-Based Systems

2011 ◽  
pp. 210-233 ◽  
Author(s):  
Michael Bowman
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Intelligent Agents ◽  
Expert Knowledge ◽  
Rule Learning ◽  
Subject Matter Experts ◽  
Agent Based ◽  
Mixed Initiative ◽  
The Military ◽  
Modeling Expert

For intelligent agents to become truly useful in real-world applications, it is necessary to identify, document, and integrate into them the human knowledge used to solve real-world problems. This article describes a methodology for modeling expert problem-solving knowledge that supports ontology import and development, teaching-based agent development, and agent-based problem solving. It provides practical guidance to subject matter experts on expressing how they solve problems using the task reduction paradigm. It identifies the concepts and features to be represented in an ontology; identifies tasks to be represented in a knowledge base; guides rule learning/refinement; supports natural language generation; and is easy to use. The methodology is applicable to a wide variety of domains and has been successfully used in the military domain. This research is part of a larger effort to develop an advanced approach to expert knowledge acquisition based on apprenticeship multi-strategy learning in a mixed-initiative framework.

Reasoning based on consolidated real world experience acquired by a humanoid robot

2016 ◽  
Vol 17 (2) ◽  
pp. 248-278 ◽  
Author(s):  
Maxime Petit ◽  
Grégoire Pointeau ◽  
Peter Ford Dominey
Keyword(s):  
Problem Solving ◽  
Human Development ◽  
Real World ◽  
Humanoid Robot ◽  
Expert Knowledge ◽  
Proof Of Concept ◽  
Tower Of Hanoi ◽  
Human Interactions ◽  
Reasoning Systems ◽  
The Individual

Abstract The development of reasoning systems exploiting expert knowledge from interactions with humans is a non-trivial problem, particularly when considering how the information can be coded in the knowledge representation. For example, in human development, the acquisition of knowledge at one level requires the consolidation of knowledge from lower levels. How is the accumulated experience structured to allow the individual to apply knowledge to new situations, allowing reasoning and adaptation? We investigate how this can be done automatically by an iCub that interacts with humans to acquire knowledge via demonstration. Once consolidated, this knowledge is used in further acquisitions of experience concerning preconditions and consequences of actions. Finally, this knowledge is translated into rules that allow reasoning and planning for novel problem solving, including a Tower of Hanoi scenario. We thus demonstrate proof of concept for an interaction system that uses knowledge acquired from human interactions to reason about new situations.

Systems theories

2018 ◽  
Author(s):  
Marc J. Stern
Keyword(s):  
Social Networks ◽  
Problem Solving ◽  
Natural Resources ◽  
Real World ◽  
Common Pool Resources ◽  
Ecological Systems ◽  
Effective Governance ◽  
Social Ecological Systems ◽  
Social Ecological ◽  
Real World Problem

This chapter covers systems theories relevant to understanding and working to enhance the resilience of social-ecological systems. Social-ecological systems contain natural resources, users of those resources, and the interactions between each. The theories in the chapter share lessons about how to build effective governance structures for common pool resources, how to facilitate the spread of worthwhile ideas across social networks, and how to promote collaboration for greater collective impacts than any one organization alone could achieve. Each theory is summarized succinctly and followed by guidance on how to apply it to real world problem solving.

scholarly journals Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 681
Author(s):  
László Barna Iantovics
Keyword(s):  
Problem Solving ◽  
Scientific Literature ◽  
Real Life ◽  
Intelligent Agent ◽  
Difficult Problem ◽  
Black Box ◽  
Machine Intelligence ◽  
Agent Based ◽  
Np Hard Problem ◽  
Box Method

Current machine intelligence metrics rely on a different philosophy, hindering their effective comparison. There is no standardization of what is machine intelligence and what should be measured to quantify it. In this study, we investigate the measurement of intelligence from the viewpoint of real-life difficult-problem-solving abilities, and we highlight the importance of being able to make accurate and robust comparisons between multiple cooperative multiagent systems (CMASs) using a novel metric. A recent metric presented in the scientific literature, called MetrIntPair, is capable of comparing the intelligence of only two CMASs at an application. In this paper, we propose a generalization of that metric called MetrIntPairII. MetrIntPairII is based on pairwise problem-solving intelligence comparisons (for the same problem, the problem-solving intelligence of the studied CMASs is evaluated experimentally in pairs). The pairwise intelligence comparison is proposed to decrease the necessary number of experimental intelligence measurements. MetrIntPairII has the same properties as MetrIntPair, with the main advantage that it can be applied to any number of CMASs conserving the accuracy of the comparison, while it exhibits enhanced robustness. An important property of the proposed metric is the universality, as it can be applied as a black-box method to intelligent agent-based systems (IABSs) generally, not depending on the aspect of IABS architecture. To demonstrate the effectiveness of the MetrIntPairII metric, we provide a representative experimental study, comparing the intelligence of several CMASs composed of agents specialized in solving an NP-hard problem.

Alice in the Real World

2012 ◽  
Vol 17 (7) ◽  
pp. 410-416 ◽  
Author(s):  
Tom Parker
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Mathematical Problem ◽  
Computer Application ◽  
Mathematical Problem Solving ◽  
The Real ◽  
Programming Knowledge

A computer application promotes programming knowledge and allows students to create their own worlds through mathematical problem solving.

From Problem-Solving Paradigm to Co-Ontogenic Drift: How Do Learning Narratives Self-Generate?

Leonardo ◽  
2011 ◽  
Vol 44 (2) ◽  
pp. 133-138
Author(s):  
Johann van der Merwe ◽  
Julia Brewis
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Design Education ◽  
Design Theory ◽  
Social Situation ◽  
Theory And Practice ◽  
Use Of Self ◽  
Real World Problems

It is now an accepted maxim in design theory and practice that real-world problems needing the attention of design practitioners are not neat and well-structured, but ill-structured and “wicked”—part of a larger, complex social situation. For design education, then, to take its lead from contemporary social, political and economic structures, it will have to seriously re-think its problem-solving paradigms. The authors investigate the use of self-generating learning narratives in the classroom and contrast the approach they introduce with the still-too-prevalent notion that knowledge can be transferred from teacher to student. Their methodology draws from ideas formulated by Maturana and Varela on autopoiesis, specifically the notion of co-ontogenic drift.

Boosting branch-and-bound MaxSAT solvers with clause learning

2021 ◽  
pp. 1-21
Author(s):  
Chu-Min Li ◽  
Zhenxing Xu ◽  
Jordi Coll ◽  
Felip Manyà ◽  
Djamal Habet ◽  
...  
Keyword(s):  
Experimental Investigation ◽  
Combinatorial Optimization ◽  
Problem Solving ◽  
Branch And Bound ◽  
Real World ◽  
Optimization Problems ◽  
Satisfiability Problem ◽  
Maximum Satisfiability ◽  
Combinatorial Optimization Problems ◽  
Clause Learning

The Maximum Satisfiability Problem, or MaxSAT, offers a suitable problem solving formalism for combinatorial optimization problems. Nevertheless, MaxSAT solvers implementing the Branch-and-Bound (BnB) scheme have not succeeded in solving challenging real-world optimization problems. It is widely believed that BnB MaxSAT solvers are only superior on random and some specific crafted instances. At the same time, SAT-based MaxSAT solvers perform particularly well on real-world instances. To overcome this shortcoming of BnB MaxSAT solvers, this paper proposes a new BnB MaxSAT solver called MaxCDCL. The main feature of MaxCDCL is the combination of clause learning of soft conflicts and an efficient bounding procedure. Moreover, the paper reports on an experimental investigation showing that MaxCDCL is competitive when compared with the best performing solvers of the 2020 MaxSAT Evaluation. MaxCDCL performs very well on real-world instances, and solves a number of instances that other solvers cannot solve. Furthermore, MaxCDCL, when combined with the best performing MaxSAT solvers, solves the highest number of instances of a collection from all the MaxSAT evaluations held so far.

Defence Anaesthesia transition from the Tri-Service Anaesthetic Apparatus to the Diamedica Portable Anaesthesia Machine 02

2018 ◽  
Vol 165 (5) ◽  
pp. 351-355
Author(s):  
Jonathan David Pearson ◽  
A Maund ◽  
CP Jones ◽  
E Coley ◽  
S Frazer ◽  
...  
Keyword(s):  
Positive Pressure ◽  
High Fidelity Simulation ◽  
Subject Matter Experts ◽  
Simple Method ◽  
Anaesthesia Machine ◽  
Anaesthesia Equipment ◽  
Ease Of Access ◽  
The Uk ◽  
The Military ◽  
Live Patient

Defence Anaesthesia is changing its draw-over anaesthetic capability from the Tri-Service Anaesthetic Apparatus (TSAA) to the Diamedica Portable Anaesthesia Machine 02 (DPA02). The DPA02 will provide a portable, robust, lightweight and simple method for delivering draw-over volatile anaesthesia with the option of positive pressure ventilation through manual or mechanical operation for paediatric and adult patients. The UK Defence Medical Services uses a modified configuration of the DPA02; this paper seeks to explain the rationale for the differing configurations and illustrates alternative assemblies to support integration with other Defence Anaesthesia equipment. High-fidelity simulation training using the DPA02 will continue to be delivered on the Defence Anaesthesia Simulation Course (DASC). Conformité Européenne accreditation of DPA02 supports future UK live patient training in centres of excellence supervised by subject matter experts; this was not possible with the TSAA. This article is intended to be a key reference for all members of the Defence Anaesthesia team. Alongside other resources, it will be given as precourse learning prior to attending the DASC and the Military Operational Surgical Training. This article will also be issued with all Defence DPA02 units, supporting ease of access for review during future clinical exercises (including validation), prior to supervised live training and on operational deployments.

scholarly journals Data assimilation in Agent-based models using creation and annihilation operators

2019 ◽  
Author(s):  
Daniel Tang
Keyword(s):  
Real World ◽  
Null Model ◽  
The State ◽  
Actual State ◽  
Agent Based Models ◽  
Agent Based ◽  
Predator Prey ◽  
Predator Prey Model ◽  
The One ◽  
The Relationship

Agent-based models are a powerful tool for studying the behaviour of complex systems that can be described in terms of multiple, interacting ``agents''. However, because of their inherently discrete and often highly non-linear nature, it is very difficult to reason about the relationship between the state of the model, on the one hand, and our observations of the real world on the other. In this paper we consider agents that have a discrete set of states that, at any instant, act with a probability that may depend on the environment or the state of other agents. Given this, we show how the mathematical apparatus of quantum field theory can be used to reason probabilistically about the state and dynamics the model, and describe an algorithm to update our belief in the state of the model in the light of new, real-world observations. Using a simple predator-prey model on a 2-dimensional spatial grid as an example, we demonstrate the assimilation of incomplete, noisy observations and show that this leads to an increase in the mutual information between the actual state of the observed system and the posterior distribution given the observations, when compared to a null model.

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