1.
References Computational epistemology: an overview Danilo DantasComputational epistemology: an overview
2.
ReferencesPART I: Which epistemology?Computational epistemology: an overview
3.
ReferencesQuine’s proposal The stimulation of his sensory receptors is all the evidence anybody has had to go on, ultimately, in arriving at his picture of the world. Why not just see how this construction really proceeds? Why not settle for psychology? (Quine, 1969, p. 75). Epistemology, or something like it, simply falls into place as a chapter of psychology and hence of natural science. (Quine, 1969, p. 82).Computational epistemology: an overview
4.
ReferencesNaturalized versus traditional epistemology Aim Method Reduction Traditional epistemology normative a priori no Naturalized epistemology descriptive empirical yes∗ Table: The ∗ is true of some naturalized epistemologies (e.g. Quine, 1969), but not of all (e.g. Goldman, 1986).Computational epistemology: an overview
5.
ReferencesThe desirable traits to epistemology 1. To be normative-grounding; 2. To employ non-controversial methods; 3. To be emancipated, but to beneﬁt from empirical data.Computational epistemology: an overview
7.
ReferencesApproaches to AI Thinking Humanly Thinking Rationally “[The automation of] activities “The study of the computations that we associate with human that make it possible to perceive, thinking, activities such as reason, and act” (Winston, 1970). decision-making, problem solving, learning (...)” (Bellman, 1978). Acting Humanly Acting Rationally “The creation of machines that “Computational Intelligence is perform functions that require in- the study of the design of intelli- telligence when performed by peo- gent agents” (Poole et al., 1998). ple” (Kurzweil, 1990). Table: Russell and Norvig (2010)Computational epistemology: an overview
8.
ReferencesEpistemology as the description of the ideal agent The ideal (but ﬁnite) rational agent is a ﬁnite rational agent which acts to achieve the best expected outcome in all possible environments, and which does it using the less possible amount of processing and time.Computational epistemology: an overview
9.
ReferencesThe desirable traits to epistemology 1. To be normative-grounding; 2. To employ non-controversial methods; 3. To be emancipated, but to beneﬁt from empirical data.Computational epistemology: an overview
10.
ReferencesNormative-grounding S has grounds to believe that p in s ←→ The ideal agent believes that p in s S is warranted to believe that p in s S is justiﬁed to believe that p in s S has reason to believe that p in s S knows that p in s ←→ S believes that p in s & p is true & The ideal agent believes that p in sComputational epistemology: an overview
11.
ReferencesPART III: Methods and an exampleComputational epistemology: an overview
12.
ReferencesWhat is 2SAT? 2SAT is the problem of determining whether a given propositional logic formula in two-conjunctive normal form (2CNF) is satisﬁable of and providing an assignment that satisﬁes it. E.g. does any assignment satisﬁes (C ∨ ¬D) ∧ (A ∨ B) ∧ (¬A ∨ ¬C)?Computational epistemology: an overview
13.
ReferencesFormalizing problems If a problem can be described as a search problem, we may use the formalization in proposed by Russell and Norvig (2010, p. 66): The initial state; A function which returns the available actions in a given state; A transition model, which speciﬁes the result of a given action in a given state; The goal test, which determines whether a state is a goal state. A path cost function, which takes a list of pairs state-actions and returns a number.Computational epistemology: an overview
14.
References2SAT as a search problem The initial state is [x1 , ..., xn ], where x1 = x2 = ... = xn = 1. The available actions are to change the value of any number of constants pi from 0 to 1 or from 1 to 0. The transition model returns, for each action, the state with the resulting assignment. The goal test is whether an assignment render the formula true (classical logic rules). The path cost function returns the number of changes in the truth value of constants.Computational epistemology: an overview
15.
References2SAT as a graph S G [1, 1, 1] 3 1 [0, 0, 0] [1, 1, 0] 2 1 [0, 0, 1] [1, 0, 1] 2 2 1 G [0, 1, 0] [1, 0, 0] [0, 1, 1]Computational epistemology: an overview
16.
ReferencesBuilding agents The design and test of a putative ideal agent have 3 stages: 1. The choice of a hypothesis to the ideal agent for a given problem, and the building of a model of the agent based in this hypothesis; 2. The implementation of the model in a computer simulation; 3. The analysis of the data from the simulation.Computational epistemology: an overview
17.
ReferencesThe agents for 2SAT Truth table agent; Truth line agent; Simpliﬁcation agent.Computational epistemology: an overview
18.
ReferencesAnalyzing the agents 1 In order to be implementable as a model of the ideal agent, an agent must meet some requirements: 1. to have consistent dispositions; 2. to be translatable into a programming language; 3. to be computationally accurate and feasible.Computational epistemology: an overview
19.
ReferencesAnalyzing the agents 2 In analyzing data, there are 5 important measures: 1. the accuracy rate; 2. the solution cost; 3. the time and space requirements; 4. the lower bounds.Computational epistemology: an overview
26.
ReferencesPART IV: CE and other sciencesComputational epistemology: an overview
27.
ReferencesThe desirable traits to epistemology 1. To be normative-grounding; 2. To employ non-controversial methods; 3. To be emancipated, but to beneﬁt from empirical data.Computational epistemology: an overview
28.
ReferencesIs this still philosophy?Computational epistemology: an overview
29.
ReferencesPART V: The 2nd year paper XComputational epistemology: an overview
30.
ReferencesThe Bayesian agent 1. The Bayesian agent holds degrees of belief in accordance with the axioms of the probability calculus; 2. The Bayesian agent employs traditional probability calculus tools to calculate degrees of belief; 2.1 In particular, in acquiring new data, the Bayesian agent updates (some of) its old degrees upon these data using Bayes theorem. ∗ 3. The Bayesian agent holds beliefs in propositions when it degrees of belief in that proposition is higher than a threshold.Computational epistemology: an overview
31.
ReferencesThe defeasible agent (Pollock, 1995) 1. The defeasible agent adopts beliefs in response to construing arguments, provided no defeaters have already been adopted for any step of the argument; 2. The defeasible agent must keep track of the basis upon which its beliefs are held; 3. The defeasible agent must keep track of defeated inferences, and when a defeater is itself retracted, this should reinstate the defeasible inference.Computational epistemology: an overview
32.
ReferencesThe Wumpus world Bree z e PIT 4 Stench Bree z e 3 Stench PIT Bree z e Gold Bree z e 2 Stench Bree z e Bree z e 1 PIT START 1 2 3 4 Figure: Russell and Norvig (2010)Computational epistemology: an overview
33.
ReferencesReferences Bellman, R. E. (1978). An Inrrocluction to Artiﬁcial Intelligence: Can Computer Think? Boyd & Fraser Publishing Company, San Francisco. Goldman, A. (1986). Epistemology and Cognition. Cambridge: Harvard University Press. Kurzweil, R. (1990). The Age of Intelligent Machines. MIT Press, Cambridge, Massachusetts. Pollock, J. L. (1995). Cognitive carpentry: a blueprint for how to build a person. The MIT Press. Poole, D., Mackworth, A. K., and Goebel, R. (1998). Computational intelligence: A logical approach. Oxford University Press, Oxford, UK. Quine, W. V. (1969). Ontological Relativity and Other Essays, chapter Epistemology Naturalized, pages 69–90. New York: Columbia UP. Russell, S. and Norvig, P. (2010). Artiﬁcial Intelligence: A Modern Approach 3rd Edition. Upper Saddle River,EUA: Prentice-Hall. Winston, P. H. (1970). Learning structural descriptions from examples. technical report mac-tr-76. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cam- bridge, Massachusetts.Computational epistemology: an overview
A particular slide catching your eye?
Clipping is a handy way to collect important slides you want to go back to later.
Be the first to comment