5 research outputs found
A global workspace framework for combined reasoning
Artificial Intelligence research has produced
many effective techniques for solving a wide range
of problems. Practitioners tend to concentrate their efforts in one particular problem solving
paradigm and, in the main, AI research describes new methods for solving particular types of
problems or improvements in existing approaches. By contrast, much less research has considered
how to fruitfully combine different problem solving techniques. Numerous studies have
demonstrated how a combination of reasoning approaches can improve the effectiveness of one of
those methods. Others have demonstrated how, by using several different reasoning techniques,
a system or method can be developed to accomplish a novel task, that none of the individual
techniques could perform. Combined reasoning systems, i.e., systems which apply disparate
reasoning techniques in concert, can be more than the sum of their parts. In addition, they
gain leverage from advances in the individual methods they encompass. However, the benefits
of combined reasoning systems are not easily accessible, and systems have been hand-crafted
to very specific tasks in certain domains. This approach means those systems often suffer from
a lack of clarity of design and are inflexible to extension. In order for the field of combined reasoning
to advance, we need to determine best practice and identify effective general approaches.
By developing useful frameworks, we can empower researchers to explore the potential of combined
reasoning, and AI in general. We present here a framework for developing combined
reasoning systems, based upon Baars’ Global Workspace Theory. The architecture describes a
collection of processes, embodying individual reasoning techniques, which communicate via a
global workspace. We present, also, a software toolkit which allows users to implement systems
according to the framework. We describe how, despite the restrictions of the framework, we
have used it to create systems to perform a number of combined reasoning tasks. As well
as being as effective as previous implementations, the simplicity of the underlying framework
means they are structured in a straightforward and comprehensible manner. It also makes the
systems easy to extend to new capabilities, which we demonstrate in a number of case studies.
Furthermore, the framework and toolkit we describe allow developers to harness the parallel
nature of the underlying theory by enabling them to readily convert their implementations into
distributed systems. We have experimented with the framework in a number of application domains
and, through these applications, we have contributed to constraint satisfaction problem
solving and automated theory formation
Automated theory formation in pure mathematics
The automation of specific mathematical tasks such as theorem proving and algebraic
manipulation have been much researched. However, there have only been a few isolated
attempts to automate the whole theory formation process. Such a process involves
forming new concepts, performing calculations, making conjectures, proving theorems
and finding counterexamples. Previous programs which perform theory formation are
limited in their functionality and their generality. We introduce the HR program
which implements a new model for theory formation. This model involves a cycle of
mathematical activity, whereby concepts are formed, conjectures about the concepts
are made and attempts to settle the conjectures are undertaken.HR has seven general production rules for producing a new concept from old ones and
employs a best first search by building new concepts from the most interesting old
ones. To enable this, HR has various measures which estimate the interestingness of a
concept. During concept formation, HR uses empirical evidence to suggest conjectures
and employs the Otter theorem prover to attempt to prove a given conjecture. If this
fails, HR will invoke the MACE model generator to attempt to disprove the conjecture
by finding a counterexample. Information and new knowledge arising from the attempt
to settle a conjecture is used to assess the concepts involved in the conjecture, which
fuels the heuristic search and closes the cycle.The main aim of the project has been to develop our model of theory formation and
to implement this in HR. To describe the project in the thesis, we first motivate
the problem of automated theory formation and survey the literature in this area.
We then discuss how HR invents concepts, makes and settles conjectures and how
it assesses the concepts and conjectures to facilitate a heuristic search. We present
results to evaluate HR in terms of the quality of the theories it produces and the
effectiveness of its techniques. A secondary aim of the project has been to apply HR to
mathematical discovery and we discuss how HR has successfully invented new concepts
and conjectures in number theory
Efficient local search for Pseudo Boolean Optimization
Algorithms and the Foundations of Software technolog
Collected Papers (on Neutrosophic Theory and Applications), Volume VIII
This eighth volume of Collected Papers includes 75 papers comprising 973 pages on (theoretic and applied) neutrosophics, written between 2010-2022 by the author alone or in collaboration with the following 102 co-authors (alphabetically ordered) from 24 countries: Mohamed Abdel-Basset, Abduallah Gamal, Firoz Ahmad, Ahmad Yusuf Adhami, Ahmed B. Al-Nafee, Ali Hassan, Mumtaz Ali, Akbar Rezaei, Assia Bakali, Ayoub Bahnasse, Azeddine Elhassouny, Durga Banerjee, Romualdas Bausys, Mircea BoÈ™coianu, Traian Alexandru Buda, Bui Cong Cuong, Emilia Calefariu, Ahmet Çevik, Chang Su Kim, Victor Christianto, Dae Wan Kim, Daud Ahmad, Arindam Dey, Partha Pratim Dey, Mamouni Dhar, H. A. Elagamy, Ahmed K. Essa, Sudipta Gayen, Bibhas C. Giri, Daniela Gîfu, Noel Batista Hernández, Hojjatollah Farahani, Huda E. Khalid, Irfan Deli, Saeid Jafari, TèmÃtópé Gbóláhà n JaÃyéolá, Sripati Jha, Sudan Jha, Ilanthenral Kandasamy, W.B. Vasantha Kandasamy, Darjan KarabaÅ¡ević, M. Karthika, Kawther F. Alhasan, Giruta Kazakeviciute-Januskeviciene, Qaisar Khan, Kishore Kumar P K, Prem Kumar Singh, Ranjan Kumar, Maikel Leyva-Vázquez, Mahmoud Ismail, Tahir Mahmood, Hafsa Masood Malik, Mohammad Abobala, Mai Mohamed, Gunasekaran Manogaran, Seema Mehra, Kalyan Mondal, Mohamed Talea, Mullai Murugappan, Muhammad Akram, Muhammad Aslam Malik, Muhammad Khalid Mahmood, Nivetha Martin, Durga Nagarajan, Nguyen Van Dinh, Nguyen Xuan Thao, Lewis Nkenyereya, Jagan M. Obbineni, M. Parimala, S. K. Patro, Peide Liu, Pham Hong Phong, Surapati Pramanik, Gyanendra Prasad Joshi, Quek Shio Gai, R. Radha, A.A. Salama, S. Satham Hussain, Mehmet Șahin, Said Broumi, Ganeshsree Selvachandran, Selvaraj Ganesan, Shahbaz Ali, Shouzhen Zeng, Manjeet Singh, A. Stanis Arul Mary, DragiÅ¡a Stanujkić, Yusuf ȘubaÈ™, Rui-Pu Tan, Mirela Teodorescu, Selçuk Topal, Zenonas Turskis, Vakkas Uluçay, Norberto Valcárcel Izquierdo, V. Venkateswara Rao, Volkan Duran, Ying Li, Young Bae Jun, Wadei F. Al-Omeri, Jian-qiang Wang, Lihshing Leigh Wang, Edmundas Kazimieras Zavadskas