About me
I am a PhD student in the group working on the Wheelbarrow Project, with interests in the automatic reformulation of AI problems and combined reasoning. I completed my thesis on “An Artificial Intelligence Framework for Investigative Reasoning” in 2014.
Publications
Colton, Simon; Ramezani, Ramin; Llano, Maria Teresa The HR3 discovery system: Design decisions and implementation details Inproceedings In: Proceedings of the AISB symposium on Computational Scientific Discovery, 2014. @inproceedings{colton2014hr3, title = {The HR3 discovery system: Design decisions and implementation details}, author = { Simon Colton and Ramin Ramezani and Maria Teresa Llano}, url = {http://ccg.doc.gold.ac.uk/wp-content/uploads/2016/07/colton_aisb14b.pdf}, year = {2014}, date = {2014-01-01}, booktitle = {Proceedings of the AISB symposium on Computational Scientific Discovery}, abstract = {Automated Theory Formation is a hybrid AI technique which has been implemented in two scientific discovery systems, HR1 and HR2, both of which have been used successfully in vari- ous applications. We describe here the latest iteration in the HR se- ries, in terms of the lessons learned from the successes and failures of the previous versions, and how these lessons have informed our design choices and the implementation details of the new version. We also present two case studies: a synthetic domain mirroring an aspect of medical diagnosis, and invariant discovery in formal meth- ods. In each case, we compare HR3 with HR2 to highlight various improvements in the new version.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Automated Theory Formation is a hybrid AI technique which has been implemented in two scientific discovery systems, HR1 and HR2, both of which have been used successfully in vari- ous applications. We describe here the latest iteration in the HR se- ries, in terms of the lessons learned from the successes and failures of the previous versions, and how these lessons have informed our design choices and the implementation details of the new version. We also present two case studies: a synthetic domain mirroring an aspect of medical diagnosis, and invariant discovery in formal meth- ods. In each case, we compare HR3 with HR2 to highlight various improvements in the new version. |
Pease, Alison; Colton, Simon; Ramezani, Ramin; Charnley, John; Reed, Kate A Discussion on Serendipity in Creative Systems Inproceedings In: Proceedings of the Fourth International Conference on Computational Creativity, pp. 64–71, 2013. @inproceedings{pease2013discussion, title = {A Discussion on Serendipity in Creative Systems}, author = { Alison Pease and Simon Colton and Ramin Ramezani and John Charnley and Kate Reed}, url = {http://ccg.doc.gold.ac.uk/wp-content/uploads/2016/10/pease_iccc13.pdf}, year = {2013}, date = {2013-01-01}, booktitle = {Proceedings of the Fourth International Conference on Computational Creativity}, pages = {64--71}, abstract = {We investigate serendipity, or happy, accidental discoveries, in CC, and propose computational concepts related to serendipity. These include a focus-shift, a breakdown of serendipitous discovery into prepared mind, serendipity trigger, bridge and result and three dimensions of serendipity: chance, sagacity and value. We propose a definition and standards for computational serendipity and evaluate three creative systems with respect to our standards. We argue that this is an important notion in creativity and, if carefully developed and used with caution, could result in a valuable new discovery technique in CC.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We investigate serendipity, or happy, accidental discoveries, in CC, and propose computational concepts related to serendipity. These include a focus-shift, a breakdown of serendipitous discovery into prepared mind, serendipity trigger, bridge and result and three dimensions of serendipity: chance, sagacity and value. We propose a definition and standards for computational serendipity and evaluate three creative systems with respect to our standards. We argue that this is an important notion in creativity and, if carefully developed and used with caution, could result in a valuable new discovery technique in CC. |
Pease, Alison; Colton, Simon; Ramezani, Ramin; Smaill, Alan; Guhe, Markus Using Analogical Representations for Mathematical Concept Formation Incollection In: Model-Based Reasoning in Science and Technology, 314 , pp. 301–314, Springer Berlin Heidelberg, 2010, ISBN: 978-3-642-15222-1. @incollection{pease2010using, title = {Using Analogical Representations for Mathematical Concept Formation}, author = { Alison Pease and Simon Colton and Ramin Ramezani and Alan Smaill and Markus Guhe}, url = {http://ccg.doc.gold.ac.uk/wp-content/uploads/2016/08/pease_mbr10.pdf}, doi = {10.1007/978-3-642-15223-8}, isbn = {978-3-642-15222-1}, year = {2010}, date = {2010-01-01}, booktitle = {Model-Based Reasoning in Science and Technology}, volume = {314}, pages = {301--314}, publisher = {Springer Berlin Heidelberg}, abstract = {We argue that visual, analogical representations of mathematical concepts can be used by automated theory formation systems to develop fur- ther concepts and conjectures in mathematics. We consider the role of visual reasoning in human development of mathematics, and consider some aspects of the relationship between mathematics and the visual, including artists us- ing mathematics as inspiration for their art (which may then feed back into mathematical development), the idea of using visual beauty to evaluate math- ematics, mathematics which is visually pleasing, and ways of using the visual to develop mathematical concepts. We motivate an analogical representation of number types with examples of “visual” concepts and conjectures, and present an automated case study in which we enable an automated theory formation program to read this type of visual, analogical representation.}, keywords = {}, pubstate = {published}, tppubtype = {incollection} } We argue that visual, analogical representations of mathematical concepts can be used by automated theory formation systems to develop fur- ther concepts and conjectures in mathematics. We consider the role of visual reasoning in human development of mathematics, and consider some aspects of the relationship between mathematics and the visual, including artists us- ing mathematics as inspiration for their art (which may then feed back into mathematical development), the idea of using visual beauty to evaluate math- ematics, mathematics which is visually pleasing, and ways of using the visual to develop mathematical concepts. We motivate an analogical representation of number types with examples of “visual” concepts and conjectures, and present an automated case study in which we enable an automated theory formation program to read this type of visual, analogical representation. |
Pease, Alison; Smaill, Alan; Colton, Simon; Ireland, Andrew; Llano, Maria Teresa; Ramezani, Ramin; Grov, Gudmund; Guhe, Markus Applying Lakatos-style reasoning to AI problems Book Chapter In: Thinking Machines and the philosophy of computer science: Concepts and principles., Chapter 10, pp. 149–174, Information Science Reference, 2010, ISBN: 9781616920142. @inbook{pease2010applying, title = {Applying Lakatos-style reasoning to AI problems}, author = {Alison Pease and Alan Smaill and Simon Colton and Andrew Ireland and Maria Teresa Llano and Ramin Ramezani and Gudmund Grov and Markus Guhe}, url = {http://ccg.doc.gold.ac.uk/wp-content/uploads/2016/08/pease_tm10-1.pdf}, doi = {10.4018/978-1-61692-014-2.ch010}, isbn = {9781616920142}, year = {2010}, date = {2010-01-01}, booktitle = {Thinking Machines and the philosophy of computer science: Concepts and principles.}, journal = {Thinking Machines and the philosophy of computer science: Concepts and principles}, pages = {149--174}, publisher = {Information Science Reference}, chapter = {10}, abstract = {One current direction in AI research is to focus on combining different reasoning styles such as deduction, induction, abduction, analogical reasoning, non-monotonic reasoning, vague and uncertain reasoning. The philosopher Imre Lakatos produced one such theory of how people with different reasoning styles collaborate to develop mathematical ideas. Lakatos argued that mathematics is a quasi-empirical, flexible, fallible, human endeavour, involving negotiations, mistakes, vague concept definitions and disagreements, and he outlined a heuristic approach towards the subject. In this chapter we apply these heuristics to the AI domains of evolving requirement specifi- cations, planning and constraint satisfaction problems. In drawing analogies between Lakatos’s theory and these three domains we identify areas of work which correspond to each heuristic, and suggest extensions and further ways in which Lakatos’s philoso- phy can inform AI problem solving. Thus, we show how we might begin to produce a philosophically-inspired AI theory of combined reasoning.}, keywords = {}, pubstate = {published}, tppubtype = {inbook} } One current direction in AI research is to focus on combining different reasoning styles such as deduction, induction, abduction, analogical reasoning, non-monotonic reasoning, vague and uncertain reasoning. The philosopher Imre Lakatos produced one such theory of how people with different reasoning styles collaborate to develop mathematical ideas. Lakatos argued that mathematics is a quasi-empirical, flexible, fallible, human endeavour, involving negotiations, mistakes, vague concept definitions and disagreements, and he outlined a heuristic approach towards the subject. In this chapter we apply these heuristics to the AI domains of evolving requirement specifi- cations, planning and constraint satisfaction problems. In drawing analogies between Lakatos’s theory and these three domains we identify areas of work which correspond to each heuristic, and suggest extensions and further ways in which Lakatos’s philoso- phy can inform AI problem solving. Thus, we show how we might begin to produce a philosophically-inspired AI theory of combined reasoning. |
Ramezani, Ramin; Colton, Simon Automatic Generation of Dynamic Investigation Problems Inproceedings In: Proceedings of the Automated Reasoning Workshop, pp. 34, Citeseer 2010. @inproceedings{ramezani2010automatic, title = {Automatic Generation of Dynamic Investigation Problems}, author = { Ramin Ramezani and Simon Colton}, url = {http://ccg.doc.gold.ac.uk/wp-content/uploads/2016/08/ramezani_arw10.pdf}, year = {2010}, date = {2010-01-01}, booktitle = {Proceedings of the Automated Reasoning Workshop}, pages = {34}, organization = {Citeseer}, abstract = {One of the ultimate goals of AI computer programs is to solve real world problems as efficiently, or even better than people; sometimes to even solve problems that cannot be solved by people. Imagine a crime case with many suspects involved where each of the suspects has various motivations for the murder which makes the case fairly complicated and the amount of information would be large for a detective to process. Considering that the knowledge about the crime may not even be sufficient for the detective to deduce the murderer, he/she may refer to previously solved cases which bear resemblance to the current one, hoping to find information that can be generalized to the present problem. Employing this new information may lead to identifying the murderer or to at least making it easier by excluding some of the suspects. We call such problems investigation problems, (IPs). These may exhibit ambiguity and complexity but AI problem solving techniques such as machine learning, constraint solving and automated theorem proving are considered as powerful tools for solving such problems. Having focused on investigation problems inspired us to initially come up with a formalized way of defining IPs where we present here. Furthermore, we will discuss an experiment in which different scenarios of a certain IP is generated and is solved by a Constraint Satisfaction Problem (CSP) solving approach.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } One of the ultimate goals of AI computer programs is to solve real world problems as efficiently, or even better than people; sometimes to even solve problems that cannot be solved by people. Imagine a crime case with many suspects involved where each of the suspects has various motivations for the murder which makes the case fairly complicated and the amount of information would be large for a detective to process. Considering that the knowledge about the crime may not even be sufficient for the detective to deduce the murderer, he/she may refer to previously solved cases which bear resemblance to the current one, hoping to find information that can be generalized to the present problem. Employing this new information may lead to identifying the murderer or to at least making it easier by excluding some of the suspects. We call such problems investigation problems, (IPs). These may exhibit ambiguity and complexity but AI problem solving techniques such as machine learning, constraint solving and automated theorem proving are considered as powerful tools for solving such problems. Having focused on investigation problems inspired us to initially come up with a formalized way of defining IPs where we present here. Furthermore, we will discuss an experiment in which different scenarios of a certain IP is generated and is solved by a Constraint Satisfaction Problem (CSP) solving approach. |
Ramezani, Ramin; Colton, Simon Solving Mutilated Problems Inproceedings In: Automated Reasoning Workshop, pp. 27, 2009. @inproceedings{ramezani2009solving, title = {Solving Mutilated Problems}, author = { Ramin Ramezani and Simon Colton}, url = {http://ccg.doc.gold.ac.uk/wp-content/uploads/2016/08/ramezani_arw09.pdf}, year = {2009}, date = {2009-01-01}, booktitle = {Automated Reasoning Workshop}, pages = {27}, abstract = {Constraint solving, theorem proving and machine learning provide powerful techniques for solving AI problems. In all these approaches, information known as background knowledge needs to be provided, from which the system will infer new knowledge. Often, however, the background information may be obscure or incomplete, and is usually presented in a form suitable for only one type of problem solver, such as a first order theorem prover. In real world scenarios, there may not be enough background information for any single solver to solve the problem, and we are interested in cases where it may be possible to combine a machine learner, theorem prover and constraint solver in order to best use their incomplete background knowledge to solve the problem. We present here some preliminary experiments designed to test the feasibility of such an approach}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Constraint solving, theorem proving and machine learning provide powerful techniques for solving AI problems. In all these approaches, information known as background knowledge needs to be provided, from which the system will infer new knowledge. Often, however, the background information may be obscure or incomplete, and is usually presented in a form suitable for only one type of problem solver, such as a first order theorem prover. In real world scenarios, there may not be enough background information for any single solver to solve the problem, and we are interested in cases where it may be possible to combine a machine learner, theorem prover and constraint solver in order to best use their incomplete background knowledge to solve the problem. We present here some preliminary experiments designed to test the feasibility of such an approach |