By David Leake
Case-based reasoning (CBR) is a flourishing paradigm for reasoning and studying in synthetic intelligence, with significant learn efforts and burgeoning purposes extending the frontiers of the field.This publication presents an advent for college students in addition to an up to date review for skilled researchers and practitioners. It examines the sphere in a ''case-based'' method, via concrete examples of the way key matters -- together with indexing and retrieval, case model, evaluate, and alertness of CBR tools -- are being addressed within the context of more than a few projects and domain names. Complementing those case experiences are commentaries via major researchers at the classes realized from reports with CBR and visions for the jobs within which case-based reasoning could have the best impact.A instructional advent through Janet Kolodner, one of many originators of CBR, and David Leake makes the e-book obtainable to scholars and builders commencing to follow case-based reasoning. the quantity may also function an appropriate better half for a CBR or introductory AI textbook.
Read Online or Download Case-based reasoning : experiences, lessons & future directions PDF
Similar intelligence & semantics books
The Artificial Life Route To Artificial Intelligence: Building Embodied, Situated Agents
This quantity is the direct results of a convention during which a couple of best researchers from the fields of synthetic intelligence and biology amassed to envision even if there has been any floor to imagine new AI paradigm was once forming itself and what the fundamental constituents of this new paradigm have been.
An Introduction to Computational Learning Theory
Emphasizing problems with computational potency, Michael Kearns and Umesh Vazirani introduce a few critical themes in computational studying concept for researchers and scholars in man made intelligence, neural networks, theoretical desktop technological know-how, and data. Computational studying concept is a brand new and quickly increasing region of analysis that examines formal versions of induction with the pursuits of getting to know the typical equipment underlying effective studying algorithms and picking out the computational impediments to studying.
Ontology-Based Multi-Agent Systems
The Semantic internet has given loads of impetus to the improvement of ontologies and multi-agent structures. numerous books have seemed which debate the advance of ontologies or of multi-agent structures individually all alone. The becoming interplay among agnets and ontologies has highlighted the necessity for built-in improvement of those.
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
The tough and fuzzy set methods awarded right here open up many new frontiers for persevered learn and improvement. Computational Intelligence and have choice offers readers with the historical past and basic principles in the back of characteristic choice (FS), with an emphasis on ideas in keeping with tough and fuzzy units.
- Paradigms of Artificial Intelligence Programming. Case Studies in Common Lisp
- Intelligent Educational Machines: Methodologies and Experiences
- Learning with recurrent neural networks
- Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks
Extra resources for Case-based reasoning : experiences, lessons & future directions
Example text
2 The preliminary requirements phase MAS metamodel Fig. 3 The final requirements phase MAS metamodel Fig. 4 The analysis phase MAS metamodel During the Analysis Phase (see Fig. 4), the entities are characterized as passive or active and their interactions are described. The work product obtained enables an AMAS analyst to conclude on the adequacy (or not) of the AMAS to deal with the problem. If the result is positive, all the interactions between the entities are described and cooperation failures are identified.
AMAS Designer: An AMAS designer is responsible for nominal behaviour of agents during the Define Nominal Behaviour activity, cooperative behaviour 52 N. Bonjean et al. Fig. 40 Flow of tasks of the Define Module View activity of agents in the Define Cooperative Behaviour activity and fast prototyping during the Validate Design Phase activity. Indeed, from the structure analysis and the communication acts previously detailed, an AMAS designer defines skills, aptitudes, an interaction language, a world representation, a criticality and the characteristics of an agent.
27, and Fig. 28 depicts this phase according to documents, roles and work products involved. 1 Process Roles Two roles are involved in the Analysis Phase: the MAS Analyst and AMAS Analyst. • MAS Analyst: An MAS analyst is responsible for detailing the MAS Environment in Analysis Domain Characteristics activity. It consists in (1) the identification of what are the entities which are active and the ones which are not (passive), (2) the identification of the interactions between the entities. An MAS analyst is also responsible for Identify agent of the step which consists in defining autonomy, goal and negotiation abilities of active entities.