AlphaGo, Locked Strategies, and Eco-Cognitive Openness
1. Are AlphaGo Cognitive Strategies Locked? An Abductive Framework
1.1. Abduction and AI
The increasingly popular image of functional, computational, and information-processing approaches to mind as flesh-eating demons is thus subtly misplaced. For rather than necessarily ignoring the body, such approaches may instead help target larger organizational wholes in ways that help reveal where, why, how, and even how much […] embodiment and environmental embedding really matter for the construction of mind and experience.
1.2. Abduction and AlphaGo
2. Natural, Artificial, and Computational Games
2.1. Locked and Unlocked Strategies in Natural and Artificial Frameworks
2.2. Reading Ahead
- Clusters of moves to be adopted and their potential outcomes. The available scenario at time , exhibited by the board, represents an adumbration10 of a subsequent potential more profitable scenario at time , which indeed is abductively credibly hypothesized: in turn, one more abduction is selected and actuated, which—consistently and believably—activates a particular move that can lead to an envisaged more fruitful scenario.
- Possible countermoves to each move.
- Further chances after each of those countermoves. It seems that some of the smarter players of the game can read up to 40 moves ahead even in hugely complex positions.
The problems in this book are almost all reading problems. […] they are going to ask you to work out sequences of moves that capture, cut, link up, make good shape, or accomplish some other clear tactical objective. A good player tries to read out such tactical problems in his head before he puts the stones on the board. He looks before he leaps. Frequently he does not leap at all; many of the sequences his reading uncovers are stored away for future reference, and in the end never carried out. This is especially true in a professional game, where the two hundred or so moves played are only the visible part of an iceberg of implied threats and possibilities, most of which stays submerged. You may try to approach the game at that level, or you may, like most of us, think your way from one move to the next as you play along, but in either case it is your reading ability more than anything else that determines your rank.( p. 6)
3. Locked Abductive Strategies Counteract the Maximization of Eco-Cognitive Openness
4. Locking Strategies Restricts Creativity
- Contrarily to the case of high level “human” creative abductive inferences such as the ones expressed by scientific discovery or other examples of special exceptional intellectual results, the status of artificial games (and of their computational counterpart) is very poor from the point of view of the non-strategic knowledge involved. We are dealing with stones, a modest number of rules, and one board. When the game progresses, the shape of the scenario is spectacularly modified but no unexpected cognitive mediators (objects) are appearing: for example, no diversely colored stones, or a strange hexagonal board. On the contrary, to continue with the example of high levels creative abductions in scientific discovery (for example, in empirical science), first of all the evidence is extremely rich and endowed with often unexpected novel features (not only due to modifications of aspects of the “same things”, as in the case of artificial games). Secondly, the flux of knowledge at play is multifarious and is related to new analogies, thought experiments, models, imageries, mathematical structures, etc. that are rooted in heterogeneous disciplines and fields of intellectual research. In sum, in this exemplary case, we are facing with a real tendency to a status of optimal eco-cognitive situatedness (further details on this kind of creative abduction are furnished in [55,63,66]).
- What happens when we are dealing with selective abduction (for example in medical diagnosis)? First of all, evidence freely and richly arrives from several empirical sources in terms of body symptoms and data mediated by sophisticated artifacts (which also change and improve thanks to new technological inventions). Second, the encyclopedia of biomedical hypotheses in which selective abduction can work is instead locked,19 but the reference to possible new knowledge (locally created of externally available) is not prohibited, so the diagnostic inferences can be enhanced thanks to scientific advancements at a first sight not considered. Third, novel inferential strategies and linked heuristics can be created and old ones used in new surprising ways but, what is important, strategies are not locked in a fixed scenario. In sum, the creativity that is occurring in the case of human selective abduction is poorer than the one active in scientific discovery, but richer than the one related to the activity of the locked reasoning strategies of the Go game and AlphaGo, I have considered above.
- In Go (and similar games) and in deep learning systems such as AlphaGo, in which strategies and heuristics are “locked”, these are exactly the only part of the game that can be improved and rendered more fertile: strategies and related heuristics can be used in a novel way and new ones can be invented. Anticipations as abductions (which incarnate the activities of “reading ahead”) just affect the modifications and re-grouping of the same elements. No other types of knowledge will increase; all the rest remains stable.20 Of course, this dominance of the strategies is the quintessence of Go, Chess, and other games, and also reflects the spectacularity of the more expert moves of the human champions. However, it has to be said that this dominance is also the reason that explains the fact the creativity at stake is even more modest than the one involved in the higher cases of selective abduction (diagnosis). I will soon illustrate that this fact is also the reason that explains why the smart strategies of Go or Chess games can be more easily simulated, for example with respect to the inferences at play in scientific discovery, by recent artificial intelligence programs, such as the ones based on deep learning.21
Let us compare the key ideas behind Deep Blue (Chess) and AlphaGo (Go). The first program used values to assess potential moves, a function that incorporated lots of detailed chess knowledge to evaluate any given board position and immense computing power (brute force) to calculate lots of possible positions, selecting the move that would drive the best possible final possible position. Such ideas were not suitable for Go. A good program may capture elements of human intuition to evaluate board positions with good shape, an idea able to attain far-reaching consequences. After essays with Monte Carlo tree search algorithms, the bright idea was to find patterns in a high quantity of games (150,000) with deep learning based upon neural networks. The program kept making adjustments to the parameters in the model, trying to find a way to do tiny improvements in its play. And, this shift was a way out to create a policy network through billions of settings, i.e., a valuation system that captures intuition about the value of different board position. Such search-and-optimization idea was cleverer about how search is done, but the replication of intuitive pattern recognition was a big deal. The program learned to recognize good patterns of play leading to higher scores, and when that happened it reinforces the creative behavior (it acquired an ability to recognize images with similar style).
In April 2016, New Scientist obtained a copy of a data-sharing agreement between DeepMind and the Royal Free London NHS Foundation Trust. The latter operates three London hospitals where an estimated 1.6 million patients are treated annually. The agreement shows DeepMind Health had access to admissions, discharge and transfer data, accident and emergency, pathology and radiology, and critical care at these hospitals. This included personal details such as whether patients had been diagnosed with HIV, suffered from depression or had ever undergone an abortion in order to conduct research to seek better outcomes in various health conditions. A complaint was filed to the Information Commissioner’s Office (ICO), arguing that the data should be pseudonymised and encrypted. In May 2016, New Scientist published a further article claiming that the project had failed to secure approval from the Confidentiality Advisory Group of the Medicines and Healthcare Products Regulatory Agency. In May 2017, Sky News published a leaked letter from the National Data Guardian, Dame Fiona Caldicott, revealing that in her ‘considered opinion’ the data-sharing agreement between DeepMind and the Royal Free took place on an ‘inappropriate legal basis’. The Information Commissioner’s Office ruled in July 2017 that the Royal Free hospital failed to comply with the Data Protection Act when it handed over personal data of 1.6 million patients to DeepMind.
Conflicts of Interest
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The AI research on these topics also favored the formation, in two philosophy departments, of the following facilities: the Computational Epistemology Laboratory (http://cogsci.uwaterloo.ca/) headed by P. Thagard at the University of Waterloo, Canada and the Computational PhilosophyLaboratory (http://www-3.unipv.it/webphilos_lab/wordpress/), headed by myself at the University of Pavia, Italy, both devoted to research into cognitive science and related areas of philosophy.
Classical volumes where the reader can find the illustration of the most important research and of some historical machine discovery programs are Langley  and Shrager and Langley . Cf. also Zytkov  (Proceedings of MD-92 Workshop on “Machine Discovery”), and Colton  (Proceedings of AISB’99).
The proceedings are still available online https://www.researchgate.net/publication/235198935_Working_Notes_of_the_1990_Spring_Symposium_on_Automated_Abduction.
A review of the classical AI approaches to abduction (mainly based on logic programming) is given by Paul  and Bylander et al., Reiter et al., de Kleer and Williams, Reggia et al. [17,18,19,20] (set covering approaches). Other classical programs regarding discovery in science are illustrated by Valdés-Pérez : MECHEM (reaction mechanisms in chemistry ), ARROSMITH (intertwining between drugs or dietary aspects and diseases in medicine ), GRAFFITI (generation of conjectures in graph theory and other mathematical areas ), MDP/KINSHIP (determination of classes within a classification in linguistics ).
Other computational programs that demonstrated their efficaciousness in the execution of machine discovery abductive tasks derived from the studies on genetic algorithms and evolving neural networks (cf. for example [42,43])—in which creative abductive reasoning is rendered by exploiting some of the Darwinian mechanisms involved by evolutionary theories—and also from the so-called research on DNA computers .
A rich survey of the intertwining between computation and scientific explanation and abductive discovery is illustrated in Thagard and Litt .
A survey about the importance of models in abductive cognition is illustrated in .
The need of a plurality of representations was already clear at the time of classical AI formalisms, when I was collaborating with AI researchers to implement a Knowledge-Based System (KBS) able to develop medical abductive reasoning .
Cf. Wikipedia, entry Go (game) https://en.wikipedia.org/wiki/Go_(game).
An expressive adjective still used by Husserl . Translated by D. Carr and originally published in Husserl, E. The Crisis of European Sciences and Transcendental Phenomenology ; George Allen & Unwin and Humanities Press: London, UK; New York, NY, USA, 1970.
This expression, I have extendedly used in , is derived from Hutchins, who introduced the expression “mediating structure”, which regards external tools and props that can be constructed to cognitively enhance the activity of navigating. Written texts are trivial examples of a cognitive “mediating structure” with clear cognitive purposes, so mathematical symbols, simulations, and diagrams, which often become “epistemic mediators”, because related to the production of scientific results: “Language, cultural knowledge, mental models, arithmetic procedures, and rules of logic are all mediating structures too. So are traffic lights, supermarkets layouts, and the contexts we arrange for one another’s behavior. Mediating structures can be embodied in artifacts, in ideas, in systems of social interactions […]” ( pp. 290–291) that function as an enormous new source of information and knowledge.
Cf. Wikipedia entry Go (game) https://en.wikipedia.org/wiki/Go_(game).
I have furnished more cognitive and technical details to explain this result in .
Of course, many of the strategies of a good player are already mentally present thanks to the experience of several previous games.
Many interesting examples can be found in the recent .
It is necessary to select from pre-stored diagnostic hypotheses.
Obviously, for example, new rules and new boards can be proposed, so realizing new types of game, but this chance does not jeopardize my argumentation.
Some notes on the area of the so-called automated scientific discovery in AI cf. ( chapter 2, section 2.7 “Automatic Abductive Scientists”).
Date of access 10 of January, 2019.
Relatively recent bibliographic references can be found in my book .
On this problem and other negative epistemological use of computational programs, cf. the recent .
© 2019 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Magnani, L. AlphaGo, Locked Strategies, and Eco-Cognitive Openness. Philosophies 2019, 4, 8. https://doi.org/10.3390/philosophies4010008
Magnani L. AlphaGo, Locked Strategies, and Eco-Cognitive Openness. Philosophies. 2019; 4(1):8. https://doi.org/10.3390/philosophies4010008Chicago/Turabian Style
Magnani, Lorenzo. 2019. "AlphaGo, Locked Strategies, and Eco-Cognitive Openness" Philosophies 4, no. 1: 8. https://doi.org/10.3390/philosophies4010008