**Books**

Holt, Rinehart, and Winston, 1970.*An Introduction to Symbolic Logic*.Princeton University Press, 1974. This book is out of print, but can be downloaded as a pdf file (5 MB)*Knowledge and Justification*.Reidel, 1976. This book is out of print, but can be downloaded as a pdf file (3.3 MB)*Subjunctive Reasoning*.Princeton University Press, 1982. This book is out of print, but can be downloaded as a pdf file (5 MB)*Language and Thought*.Princeton University Press, 984. This book is out of print, but can be downloaded as a pdf file (3.9 MB)*The Foundations of Philosophical Semantics*.Rowman and Littlefield, 1986.*Contemporary Theories of Knowledge*.Bradford/MIT, 1990.*How to Build a Person*.Oxford University Press, 1990.*Nomic Probability and the Foundations of Induction*.Westview Press, 1990. This book is out of print, but can be downloaded as a pdf file (1.9 MB)*Technical Methods in Philosophy*.. Co-edited with Rob Cummins. Bradford/MIT. 1991.*Philosophy and Artificial Intelligence: Essays at the Interface*, Bradford/MIT Press, 1995.*Cognitive Carpentry***Contemporary Theories of Knowledge, 2nd edition****.**Coauthored with Joe Cruz. Rowman and Littlefield, 1999.This is an introductory symbolic logic text. I do not intend to publish it in other than electronically, and it is free to anyone to use. I would appreciate any feedback you may have if you either read it for yourself or use it in a course.**Logic: An Introduction to the Formal Study of Reasoning.**

(Oxford University Press, 2006).**Thinking about Acting: Logical Foundations for Rational Decision Making**

The objective of this book is to produce a theory of rational decision making for realistically resource-bounded agents. My interest is not in "What should I do if I were an ideal agent?", but rather, "What should I do given that I am who I am, with all my actual cognitive limitations?"

The book has three parts. Part One addresses the question of where the values come from that agents use in rational decision making. The most comon view among philosophers is that they are based on preferences, but I argue that this is computationally impossible. I propose an alternative theory somewhat reminiscent of Bentham, and explore how human beings actually arrive at values and how they use them in decision making.

Part Two investigates the knowledge of probability that is required for decision-theoretic reasoning. I argue that subjective probability makes no sense as applied to realistic agents. I sketch a theory of objective probability to put in its place. Then I use that to define a variety of causal probability and argue that this is the kind of probability presupposed by rational decision making. So what is to be defended is a variety of causal decision theory.

Part Three explores how these values and probabilities are to be used in decision making. In chapter eight, it is argued first that actions cannot be evaluated in terms of their expected values as ordinarily defined, because that does not take account of the fact that a cognizer may be unable to perform an action, and may even be unable to try to perform it. An alternative notion of "expected utility" is defined to be used in place of expected values. In chapter nine it is argued that individual actions cannot be the proper objects of decision-theoretic evaluation. We must instead choose plans, and select actions indirectly on the grounds that they are prescribed by the plans we adopt. However, our objective cannot be to find plans with maximal expected utilities. Plans cannot be meaningfully compared in that way. An alternative, called "locally global planning", is proposed. According to locally global planning, individual plans are to be assessed in terms of their contribution to the cognizer's "master plan". Again, the objective cannot be to find master plans with maximal expected utilities, because there may be none, and even if they are, finding them is not a computationally feasible task for real agents. Instead, the objective must be to find good master plans, and improve them as better ones come along. It is argued that there are computationally feasible ways of doing this, based on defeasible reasoning about values and probabilities.

View or download table of contents (pdf file)

Epistemology and Epistemic Cognition

Rational Decision Making and Practical Cognition (including decision-theoretic planning)

*New Papers:*

**"A Resource-Bounded Agent Addresses the Newcomb Problem"****"A Recursive Semantics for Defeasible Reasoning"****"Problems for Bayesian Epistemology"****"Pollock: 5 Questions for Epistemologists"****"Reasoning Defeasibly about Probabilities"****"Probable Probabilities"****"Probabilities for AI"****"Direct Inference and Probable Probabilities"****"Defeasible Reasoning"****"Epistemology, Rationality, and Cognition"****"Plans and Decisions"****.****"Against Optimality"****"What Am I? Virtual machines and the mind/body problem".**- "
**Irrationality and Cognition"**. - "
**Vision, Knowledge, and the Mystery Link"****, coauthored with Iris Oved.** - "
**So you think you exist? In defense of nolipsism."****, coauthored with Jenann Ismael.**

**The OSCAR Architecture for Rational Agents:**

**"OSCAR: An agent architecture based on defeasible reasoning."**Proceedings of the 2008 AAAI Spring Symposium on*Architectures for Intelligent Theory-Based Agents*. "OSCAR is a fully implemented architecture for a cognitive agent, based largely on the author's work in philosophy concerning epistemology and practical cognition. The seminal idea is that a generally intelligent agent must be able to function in an environment in which it is ignorant of most matters of fact. The architecture incorporates a general-purpose defeasible reasoner, built on top of an efficient natural deduction reasoner for first-order logic. It is based upon a detailed theory about how the various aspects of epistemic and practical cognition should interact, and many of the details are driven by theoretical results concerning defeasible reasoning." Download paper in pdf form.- "
**OSCAR: A cognitive architecture for intelligent agents"**. The "grand problem" of AI has always been to build artificial agents of human-level intelligence, capable of operating in environments of real-world complexity. OSCAR is a cognitive architecture for such agents, implemented in LISP. OSCAR is based on my extensive work in philosophy concerning both epistemology and rational decision making. This paper provides a detailed overview of OSCAR. The main conclusions are that such agents must be capablew of operating against a background of pervasive ignorance, because the real world is too complex for them to know more than a small fraction of what is true. This is handled by giving the agent the power to reason defeasibily. The OSCAR system of defeasible reasoning is sketched. It is argued that if epistemic cognition must be defeasible, planning must also be done defeasibly, and the best way to do that is to reason defeasibly about plans. A sketch is given about how this might work. Download paper in pdf form. **"OSCAR: An architecture for generally intelligent agents".**"OSCAR is a fully implemented architecture for a cognitive agent, based largely on the author's work in philosophy concerning epistemology and practical cognition. The seminal idea is that a generally intelligent agent must be able to function in an environment in which it is ignorant of most matters of fact. The architecture incorporates a general-purpose defeasible reasoner, built on top of an efficient natural deduction reasoner for first-order logic. It is based upon a detailed theory about how the various aspects of epistemic and practical cognition should interact, and many of the details are driven by theoretical results concerning defeasible reasoning. The architecture is easily extensible by changing the set of inference schemes supplied to the reasoner. Existing inference schemes handle many kinds of epistemic cognition, including reasoning from perceptual input, causal reasoning and the frame problem, and reasoning defeasibly about probabilities. Work is underway to implement a system of defeasible decision-theoretic planning. Download paper in pdf form.**"Rational Cognition in OSCAR".**A general overview of OSCAR, presented at the ATAL-99 conference, and published in*Proceedings of ATAL-99*, ed. N. Jennings and Y. Lesperance, Springer Verlag. The zipped Powerpoint slides can also be downloaded. Download slides ; download paper in pdf form.**"Rational thought and artificial intelligence".**Powerpoint slides of a talk given at RPI. Download slides in zipped form.**"Planning Agents".**Appeared in*Foundations of Rational Agency*, ed. Rao and Wooldridge, published by Kluwer. "It is argued that the essence of a rational agent lies in its ability to make and execute plans. Viewing planning from the perspective of rational agents requires planning and epistemic reasoning to be interleaved in ways that are impossible for standard planners. Planning must be carried out by reasoning rather than algorithmically. It is illustrated how this can be accomplished in the OSCAR architecture for rational agency. "Postscript or pdf

**"What Am I? Virtual machines and the mind/body problem".**Forthcoming in*Philosophy and Phenomenological Research*. "When your word processor or email program is running on your computer, this creates a 'virtual machine' that manipulates windows, files, text, etc. What is this virtual machine, and what are the virtual objects it manipulates? Many standard arguments in the philosophy of mind have exact analogues for virtual machines and virtual objects, but we do not want to draw the wild metaphysical conclusions that have sometimes tempted philosophers in the philosophy of mind. A computer file is not made of epiphenomenal ectoplasm. I argue instead that virtual objects are 'supervenient objects'. The stereotypical example of supervenient objects is the statue and the lump of clay. To this end I propose a theory of supervenient objects. Then I turn to persons and mental states. I argue that my mental states are virtual states of a cognitive virtual machine implemented on my body, and a person is a supervenient object supervening on his cognitive virtual machine." Download paper in pdf form.**"So you think you exist? In defense of nolipsism." Coauthored with Jenann Ismael.**In*Knowlege and Reality: Essays in Honor of Alvin Plantinga*(Kluwer), eds. Thomas Crisp, Matthew Davidson, David Vander Laan. Springer Verlag, 2004. "Human beings think of themselves in terms of a privileged non-descriptive designator: a mental "I". Such thoughts are called "de se" thoughts. The mind/body problem is the problem of deciding what kind of thing I am, and it can be regarded as arising from the fact that we think of ourselves non-descriptively. Why do we think of ourselves in this way? We investigate the functional role of "I" (and also "here" and "now") in cognition, arguing that the use of such non-descriptive "reflexive" designators is essential for making sophisticated cognition work in a general-purpose cognitive agent. If we were to build a robot capable of similar cognitive tasks as humans, it would have to be equipped with such designators. Once we understand the functional role of reflexive designators in cognition, we will see that to make cognition work properly, an agent must use a de se designator in specific ways in its reasoning. Rather simple arguments based upon how "I" works in reasoning lead to the conclusion that it cannot designate the body or part of the body. If it designates anything, it must be something non-physical. However, for the purpose of making the reasoning work correctly, it makes no difference whether "I" actually designates anything. If we were to build a robot that more or less duplicated human cognition, we would not have to equip it with anything for "I" to designate, and general physicalist inclinations suggest that there would be nothing for 'I' to designate in the robot. In particular, it cannot designate the physical contraption. So the robot would believe "I exist", but it would be wrong. Why should we think we are any different?" Download paper in pdf form.

**"Reasoning Defeasibly about Probabilities."**To appear in Michael O'Rourke and Joseph Cambell (eds.),*Knowledge and Skepticism*, Cambridge, MA: MIT Press. (This is a short version of the next paper listed.) Originally presented at the Pacific Division APA, April, 2007. "In concrete applications of probability, statistical investigation gives us knowledge of some probabilities, but we generally want to know many others that are not directly revealed by our data. For instance, we may know prob(*P*/*Q*) (the probability of*P*given*Q*) and prob(*P*/*R*), but what we really want is prob(*P*/*Q*&*R*), and we may not have the data required to assess that directly. The probability calculus is of no help here. Given prob(*P*/*Q*) and prob(*P*/*R*), it is consistent with the probability calculus for prob(*P*/*Q*&*R*) to have any value between 0 and 1. Is there any way to make a reasonable estimate of the value of prob(*P*/*Q*&*R*)? A related problem occurs when probability practitioners adopt undefended assumptions of statistical independence simply on the basis of not seeing any connection between two propositions. This is common practice, but its justification has eluded probability theorists, and researchers are typically apologetic about making such assumptions. Is there any way to defend the practice? This paper shows that on a certain conception of probability 'nomic probability' there are principles of 'probable probabilities' that license inferences of the above sort. These are principles telling us that although certain inferences from probabilities to probabilities are not deductively valid, nevertheless the second-order probability of their yielding correct results is 1. This makes it defeasibly reasonable to make the inferences. Thus I argue that it is defeasibly reasonable to assume statistical independence when we have no information to the contrary. And I show that there is a function Y(*r,s,a*) such that if prob(*P*/*Q*) =*r*, prob(*P*/*R*) =*s*, and prob(*P*/*U*) =*a*(where*U*is our background knowledge) then it is defeasibly reasonable to expect that prob(*P*/*Q*&*R*) = Y*(r,s,a*). Numerous other defeasible inferences are licensed by similar principles of probable probabilities. This has the potential to greatly enhance the usefulness of probabilities in practical application." Download paper in pdf form. Download slides from APA. Download LISP code for computing probable probabilities.-
**"Probable probabilities". "**In concrete applications of probability, statistical investigation gives us knowledge of some probabilities, but we generally want to know many others that are not directly revealed by our data. For instance, we may know prob(*P*/*Q*) (the probability of*P*given*Q*) and prob(*P*/*R*), but what we really want is prob(*P*/*Q*&*R*), and we may not have the data required to assess that directly. The probability calculus is of no help here. Given prob(*P*/*Q*) and prob(*P*/*R*), it is consistent with the probability calculus for prob(*P*/*Q*&*R*) to have any value between 0 and 1. Is there any way to make a reasonable estimate of the value of prob(*P*/*Q*&*R*)? A related problem occurs when probability practitioners adopt undefended assumptions of statistical independence simply on the basis of not seeing any connection between two propositions. This is common practice, but its justification has eluded probability theorists, and researchers are typically apologetic about making such assumptions. Is there any way to defend the practice? This paper shows that on a certain conception of probability 'nomic probability' there are principles of 'probable probabilities' that license inferences of the above sort. These are principles telling us that although certain inferences from probabilities to probabilities are not deductively valid, nevertheless the second-order probability of their yielding correct results is 1. This makes it defeasibly reasonable to make the inferences. Thus I argue that it is defeasibly reasonable to assume statistical independence when we have no information to the contrary. And I show that there is a function Y(*r*,*s*|*a*) such that if prob(*P*/*Q*) =*r*, prob(*P*/*R*) =*s*, and prob(*P*/*U*) =*a*(where*U*is our background knowledge) then it is defeasibly reasonable to expect that prob(*P*/*Q*&*R*) = Y(*r*,*s*|*a*). Numerous other defeasible inferences are licensed by similar principles of probable probabilities. This has the potential to greatly enhance the usefulness of probabilities in practical application. Download paper in pdf form. Download LISP code for computing probable probabilities. **"Probable probabilities (with proofs)"**. This is the long version of the previous paper, including additional results and the proofs of theorems. Download paper in pdf form. Download LISP code for computing probable probabilities.-
**"Probabilities for AI". "**Probability plays an essential role in many branches of AI, where it is typically assumed that we have a complete probability distribution when addressing a problem. But this is unrealistic for problems of real-world complexity. Statistical investigation gives us knowledge of some probabilities, but we generally want to know many others that are not directly revealed by our data. For instance, we may know prob(*P*/*Q*) (the probability of*P*given*Q*) and prob(*P*/*R*), but what we really want is prob(*P*/*Q*&*R*), and we may not have the data required to assess that directly. The probability calculus is of no help here. Given prob(*P*/*Q*) and prob(*P*/*R*), it is consistent with the probability calculus for prob(*P*/*Q*&*R*) to have any value between 0 and 1. Is there any way to make a reasonable estimate of the value of prob(*P*/*Q*&*R*)? A related problem occurs when probability practitioners adopt undefended assumptions of statistical independence simply on the basis of not seeing any connection between two propositions. This is common practice, but its justification has eluded probability theorists, and researchers are typically apologetic about making such assumptions. Is there any way to defend the practice? This paper shows that on a certain conception of probability 'nomic probability' there are principles of 'probable probabilities' that license inferences of the above sort. These are principles telling us that although certain inferences from probabilities to probabilities are not deductively valid, nevertheless the second-order probability of their yielding correct results is 1. This makes it defeasibly reasonable to make the inferences. Thus I argue that it is defeasibly reasonable to assume statistical independence when we have no information to the contrary. And I show that there is a function Y(*r*,*s*|*a*) such that if prob(*P*/*Q*) =*r*, prob(*P*/*R*) =*s*, and prob(*P*/*U*) =*a*(where*U*is our background knowledge) then it is defeasibly reasonable to expect that prob(*P*/*Q*&*R*) = Y(*r*,*s*|*a*). Numerous other defeasible inferences are licensed by similar principles of probable probabilities. This has the potential to greatly enhance the usefulness of probabilities in practical application. Download paper in pdf form. Download LISP code for computing probable probabilities. **"Probabilities for AI (with proofs)".**This is the long version of the previous paper, including additional results and the proofs of theorems. Download paper in pdf form. Download LISP code for computing probable probabilities.- "
**"Problems for Bayesian Epistemology"**This paper raises problems for the different strategies for making sense of subjective probability within the framework of Bayesian epistemology, in each case arguing that there is no way to do it for real cognitive agents. If subjective probability makes any sense at all, it is only for ideal agents, but that is useless for epistemological purposes. Forthcoming in*Philosophical Studies.*Download paper in pdf form. -
**"Direct Inference and Probable Probabilities"**New results in the theory of nomic probability have led to a theory of probable probabilities, which licenses defeasible inferences between probabilities that are not validated by the probability calculus. Among these are classical principles of direct inference together with some new more general principles that greatly strengthen direct inference and make it much more useful. Download paper in pdf form. **"Joint Probabilities ".**"When combining information from multiple sources and attempting to estimate the probability of a conclusion, we often find ourselves in the position of knowing the probability of the conclusion conditional on each of the individual sources, but we have no direct information about the probability of the conclusion conditional on the combination of sources. The probability calculus provides no way of computing such joint probabilities. This paper introduces a new way of combining probabilistic information to estimate joint probabilities. It is shown that on a particular conception of objective probabilities, clear sense can be made of second-order probabilities (probabilities of probabilities), and these can be related to combinatorial theorems about proportions in finite sets as the sizes of the sets go to infinity. There is a rich mathematical theory consisting of such theorems, and the theorems generate corresponding theorems about second-order probabilities. Among the latter are a number of theorems to the effect that certain inferences from probabilities to probabilities, although not licensed by the probability calculus, have probability 1 of producing correct results. This does not mean that they will always produce correct results, but the set of cases in which the inferences go wrong form a set of measure 0. Among these inferences are some enabling us to reasonably estimate the values of joint probabilities in a wide range of cases. A function called the Y-function is defined. The central theorem is the Y-Theorem, which tells us that if we know the individual probabilities for the different information sources and estimate the joint probability using the Y-function, the second-order probability of getting the right answer is 1. This mathematical result is tested empirically using a simple multi-sensor example. The Y-theorem agrees with Dempster's rule of combination in special cases, but not in general. The paper goes on to investigate cases in which the Y-theorem cannot be expected to give the right answer, and it is shown that there are generalizations of the Y-theorem that can still be employed." Download paper in pdf form.**"The Y-function ".**In*Probability and Evidence*, (ed). Greg Wheeler and Billl Harper, King's College publications, 2007. "Direct inference derives values for definite (single-case) probabilities from those of related indefinite (general) probabilities. But direct inference is less useful than might be supposed, because we often have too much information, with the result that we can make conflicting direct inferences, and hence they all undergo collective defeat, leaving us without any conclusion to draw about the value of the definite probabilities. This paper presents reason for believing that there is a function 'the Y-function' that can be used to combine different indefinite probabilities to yield a single value for the definite probability. Thus we get a kind of 'computational' direct inference." Download paper in pdf form.**"An Objectivist Argument for Thirdism"**, with the OSCAR seminar: Adam Arico, Nathan Ballantyne, Matt Bedke, Jacob Caton, Ian Evans, Don Fallis, Brian Fiala, Martin Frické, David Glick, Peter Gross, Terry Horgan, Jenann Ismael, Daniel Sanderman, Paul Thorn, Orlin Vakarelov,*Analysis*, forthcoming. Download paper in pdf form.**"Causal probability"**(*Synthese***132**(2002), 143-185).**"The theory of nomic probability".**This is a slightly revised version of "The theory of nomic probability",*Synthese*90 (1992), 263-300. It summarizes much of the material published in*Nomic Probability and the Foundations of Induction*, Oxford, 1990. Download paper in pdf form.

*Rational Decision Making, Practical Cognition, Decision-Theoretic Planning:*

**"A Resource-Bounded Agent Addresses the Newcomb Problem".**To appear in*Synthese*. "In the Newcomb problem, the standard arguments for taking either one box or both boxes adduce what seem to be relevant considerations, but they are not complete arguments, and attempts to complete the arguments rely upon incorrect principles of rational decision making. It is argued that by considering how the predictor is making his prediction, we can generate a more complete argument, and this in turn supports a form of causal decision theory." Download paper in pdf form. Download powerpoint slides for a related talk.**"Rational Decison-Making in Resource-Bounded Agents".**To appear in*How to Build Smart Machines*, in the*Rensselaer Core Debates in Cognitive Science*. "The objective of this paper is to construct an implementable theory of rational decision-making for cognitive agents subject to realistic resource constraints. It is argued that decision-making should select actions indirectly by selecting plans that prescribe them. It is also argued that although expected values provide the tool for evaluating plans, plans cannot be compared straightforwardly in terms of their expected values, and the objective of a realistic agent cannot be to find optimal plans. The theory of Locally Global planning is proposed as a realistic alternative to standard "maximizing" theories of rational decision-making." Download paper in pdf form.**"Evaluative Cognition".***Nous*(2001), 325-364. The cognition of a cognitive agent can be subdivided into two parts. Epistemic cognition is that kind of cognition responsible for producing and maintaining beliefs. Practical cognition evaluates the world, adopts plans, and initiates action. There is a massive literature both in philosophy and artificial intelligence concerning various aspects of epistemic cognition, and large parts of it are well understood. Practical cognition is less well understood. We can usefully divide practical cognition into five parts: (1) the evaluation of the world as represented by the agent's beliefs, (2) the adoption of goals for changing it, (3) the construction of plans for achieving goals, (4) the adoption of plans, and (5) the execution of plans. There is a substantial literature in AI concerning the construction and execution of plans, and I will say nothing further about those topics here. This paper will focus on the evaluative aspects of practical cognition. Evaluation plays an essential role in both goal selection and plan adoption. My concern here is the investigation of evaluation as a cognitive enterprise performed by cognitive agents. I am interested both in how it is performed in human beings and how it might be performed in artificial rational agents. Download paper in pdf form.**35****"Plans and decisions".**(*Theory and Decision***57**(2005), 79-107.) Counterexamples are constructed for classical decision theory, turning on the fact that actions must often be chosen in groups rather than individually, i.e., the objects of rational choice are plans. It is argued that there is no way to define optimality for plans that makes the finding of optimal plans the desideratum of rational decision-making. An alternative called 'locally global planning' is proposed as a replacement for classical decision theory. Decision-making becomes a non-terminating process without a precise target rather than a terminating search for an optimal solution. Download paper in pdf form.**Rational Choice and Action omnipotence"**(*Philosophical Review***111**(2003), 1-23*)*Counterexamples are constructed for classical decision theory, turning on the fact that an agent may be unable to perform an action, and may even be unable to try to perform an action. A proposal is made for how to repair classical decision theory in light of these counterexamples. Download paper in pdf form.**"Some logical conundrums for decision-theoretic contingency planning".**There are two general approaches to handling contingencies in decision-theoretic planning. State-space planners reason globally, building a map of the parts of the world relevant to the planning problem, and then attempt to distill a plan out of the map. POCL planners reason locally, attempting to build the plan up from local relationships. A planning problem is constructed that humans find trivial, but no state-space planner can solve. This motivates an investigation of decision-theoretic POCL contingency planners. Existing POCL contingency planners attempt to generalize the results of classical POCL contingency planning. However, this paper argues that the nature of contingency planning changes dramatically in decision-theoretic contexts, and results from classical contingency planning are of little relevance. In particular, in classical planning contingencies can only be attached to conditional forks, but in most uses of contingencies in decision-theoretic planning they are attached to single branches of the plan rather than to conditional forks. A criterion of adequacy for contingency planners is formulated, following from ordinary completeness, and it is shown that existing decision-theoretic POCL contingency planners do not satisfy it. Some tentative suggestions are made regarding how to construct a planner that does satisfy the adequacy condition.. Download paper in pdf form.**"Against Optimality: T****he Logical Foundations of Decision-Theoretic Planning".***Computational Intelligence***22**(2006), 1:25." This paper investigates decision-theoretic planning in sophisticated autonomous agents operating in environments of real-world complexity. An example might be a planetary rover exploring a largely unknown planet. It is argued that existing algorithms for decision-theoretic planning are based on a logically incorrect theory of rational decision making. Plans cannot be evaluated directly in terms of their expected values, because plans can be of different scopes, and they can interact with other previously adopted plans. Furthermore, in the real world, the search for optimal plans is completely intractable. An alternative theory of rational decision making is proposed, called 'locally global planning'." Download paper in pdf form.**"The Logical Foundations of Decision-Theoretic Planning". (version of 2/1/03)**Decision-theoretic planning is normally based on the assumption that plans can be compared by comparing their expected-values, and the objective is to find an optimal plan. This is typically defended by reference to classical decision theory. However, classical decision theory is actually incompatible with this "simple plan-based decision theory". A defense of plan-based decision theory must begin by showing that classical decision theory is incorrect insofar as the two theories conflict, so this paper begins by raising objections to classical decision theory . First, there is a discussion of the considerations arising out of the Newcomb problem that have given rise to causal decision theory. Next, counterexamples are constructed for classical decision theory turning on the fact that an agent may be unable to perform an action, and may even be unable to try to perform an action. A proposal is made for how to repair classical decision theory in light of these counterexamples. But then turning to the concept of an "alternative" that is presupposed by classical decision theory, it is argued that actions must often be chosen in groups rather than individually, i.e., the objects of rational choice are plans. It is argued that optimality cannot be defined for plans, and even if it could be, it would not be reasonable to require rational agents to find optimal plans. So simple plan-based decision theory must also be rejected. An alternative called "locally global planning" is proposed as a replacement for both classical decision theory and simple plan-based decision theory. Download paper in pdf form.**"An Easy "Hard Problem" for Decision-Theoretic Planning".**This paper presents a challenge problem for decision-theoretic planners. State-space planners reason globally, building a map of the parts of the world relevant to the planning problem, and then attempt to distill a plan out of the map. A planning problem is constructed that humans find trivial, but no state-space planner can solve. Existing POCL planners cannot solve the problem either, but for a less fundamental reason. Download paper in pdf form.**"Locally Global Planning".**This is a presentation at the Decision-Theoretic Planning Workshop during AIPS-2000. It is conjectured that MDP and POMDP planning will remain unfeasible for complex domains, so some form of "classical" decision-theoretic planning is sought. However, local plans cannot be properly compared in terms of their expected values, because those values will be affected by the other plans the agent has adopted. Plans must instead be merged into a single "master-plan", and new plans evaluated in terms of their contribution to the value of the master plan. To make both the construction and evaluation of plans feasible, it is proposed to evaluate plans and their interactions defeasibly. Download paper in pdf form.**"The Logical Foundations of Goal-Regression Planning in Autonomous Agents".***Artificial Intelligence*,**106**(1998), 267-335. "This paper addresses the logical foundations of goal-regression planning in autonomous rational agents. It focuses mainly on three problems. The first is that goals and subgoals will often be conjunctions, and to apply goal-regression planning to a conjunction we usually have to plan separately for the conjuncts and then combine the resulting subplans. A logical problem arises from the fact that the subplans may destructively interfere with each other. This problem has been partially solved in the AI literature (e.g., in SNLP and UCPOP), but the solutions proposed there work only when a restrictive assumption is satisfied. This assumption pertain to the computability of threats. It is argued that this assumption may fail for an autonomous rational agent operating in a complex environment. Relaxing this assumption leads to a theory of defeasible planning. The theory is formulated precisely and an implementation in the OSCAR architecture is discussed. The second problem is that goal-regression planning proceeds in terms of reasoning that runs afoul of the Frame Problem. It is argued that a previously proposed solution to the Frame Problem legitimizes goal-regression planning, but also has the consequence that some restrictions must be imposed on the logical form of goals and subgoals amenable to such planning. These restrictions have to do with temporal-projectibility. The third problem is that the theory of goal-regression planning found in the AI literature imposes restrictive syntactical constraints on goals and subgoals and on the relation of logical consequence. Relaxing these restrictions leads to a generalization of the notion of a threat, related to collective defeat in defeasible reasoning. Relaxing the restrictions also has the consequence that the previously adequate definition of "expectable-result" no longer guarantees closure under logical consequence, and must be revised accordingly. That in turn leads to the need for an additional rule for goal-regression planning. Roughly, the rule allows us to plan for the achievement of a goal by searching for plans that will achieve states that "cause" the goal. Such a rule was not previously necessary, but becomes necessary when the syntactical constraints are relaxed. The final result is a general semantics for goal-regression planning and a set of procedures that is provably sound and complete. It is shown that this semantics can easily handle concurrent actions, quantified preconditions and effects, creation and destruction of objects, and causal connections embodying complex temporal relationships." Postscript or pdf**"Reasoning Defeasibly about Plans".**OSCAR Project technical report. "Planning theory has traditionally made the assumption that the planner begins with all relevant knowledge for solving the problem. Autonomous agents cannot make that assumption. They are both planning agents and epistemic agents, and the pursuit of knowledge is driven by the planning. The search for a plan raises questions about the world and the agent must pursue answers to those questions during the course of the planning. Thus planning and epistemic investigation are interleaved. This is difficult to do with a traditional algorithmic planner. The obvious alternative is to build an agent in which planning and epistemic investigation use the same inference engine. This paper shows how to build such a planner based upon the OSCAR architecture for rational agents and using OSCAR's defeasible reasoner as the inference engine. The resulting planner constructs plans in roughly the same way as UCPOP, but does it by reasoning defeasibly about plans rather than running conventional plan search algorithms. A beneficial side effect is that the resulting planner is completely freed of the syntactical restrictions imposed by the STRIPS representation of actions. The planner can use the full power of first-order logic in representing the information used in planning." Postscript or pdf

*Reasoning: Defeasibly or Deductively:*

- "
**A Recursive Semantics for Defeasible Reasoning**", in*Argumentation in Artificial Intelligence*, ed. Iyad Rahwan and Guillermo Simari, Springer. "One of the most striking characteristics of human beings is their ability to function successfully in complex environments about which they know very little. In light of our pervasive ignorance, we cannot get around in the world just reasoning deductively from our prior beliefs together with new perceptual input. As our conclusions are not guaranteed to be true, we must countenance the possibility that new information will lead us to change our minds, withdrawing previously adopted beliefs. In this sense, our reasoning is 'defeasible'. The question arises how defeasible reasoning works, or ought to work. In particular we need rules governing what a cognizer ought to believe given a set of interacting arguments some of which defeat others. That is what is called a 'semantics' for defeasible reasoning, and this chapter will propose a new semantics that avoids certain clear counter-examples to all existing semantics." Download paper in pdf form.

**Defeasible Reasoning".***Reasoning: Studies of Human Inference and its Foundations*, ed. Jonathan Adler and Lance Rips, Cambridge University Press*.*This gives an overview of the OSCAR theory of defeasible reasoning. Download paper in pdf form.

- Skolemization and Unification in Natural Deduction, OSCAR Project technical report. "Skolemization and unification are familiar parts of the machinery of automated theorem proving. However, their use has generally been confined to systems using variants of resolution-refutation, or tableau methods, or similar methods in which the desired conclusion is negated and added to the premises, the resulting set of premises is skolemized, and then a contradiction is derived. In an earlier paper, I described a natural deduction system (OSCAR) that was noteworthy for its efficiency. However, a peculiar feature of that system was that it did not use skolemization and unification. Instead, it used "intuitive" rules of universal and existential instantiation and generalization that constructed substitution instances of quantified formulas, using all the (closed) terms that had occurred elsewhere in the argument. It has always seemed that this should be a source of considerable inefficiency, because the same reasoning will be replicated for different substitution instances. This suggests that if a version of the natural deduction system could be produced using skolemization and unification, it might be more efficient. However, it was not obvious how to use skolemization and unification in natural deduction. This paper presents a solution to that problem.".

- Implementing Defeasible Reasoning,
*Workshop on Computational Dialectics, International Conference on Formal and Applied Practical Reasoning*, Bonn, Germany, 1996.

- Justification and defeat,
*Artificial Intelligence***67**, (1994), 377-408.

- How to reason defeasibly,
*Artificial Intelligence***57**, (1992), 1-42.

- Interest driven suppositional reasoning,
*Journal of Automated Reasoning***6**, (1992), 419-462.

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**Epistemology, Rationality, and Cognition".**This is a longer version of a paper by the same title to appear in*Companion to Epistemology*, second edition, ed. Matthias Steup, Blackwells. It consists of a general sketch of my views on epistemology and how it relates to cognitive science and artificial intelligence. Download paper in pdf form.

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**Pollock -- Epistemology: 5 Questions".**My answers to the interview questions in*Epistemology: 5 Questions*, eds. Vincent Hendricks and Duncan Pritchard, Automatic Press/VIP, 2008. Download paper in pdf form.

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**Irrationality and Cognition".**Presented at the Inland Northwest Philosophy Conference on Knowledge and Skepticism, held April 30-May 2, 2004, in Moscow, ID and Pullman, WA. The strategy of this paper is to throw light on rational cognition and epistemic justification by examining irrationality. I argue that practical irrationality derives from a general difficulty we have in overriding conditioned features likings. Epistemic irrationality is possible because we are reflexive cognizers, able to reason about redirect some aspects of our own cognition. This has the consequence that practical irrationality can affect our epistemic cognition. I argue that all epistemic irrationality can be traced to this single source. The upshot is that one cannot give a theory of epistemic rationality or epistemic justification without simultaneously giving a theory of practical rationality. A consequence of this account is that a theory of rationality is a descriptive theory, describing contingent features of a cognitive architecture, and it forms the core of a general theory of "voluntary" cognition?those aspects of cognition that are under voluntary control. It also follows that most of the so-called "rules for rationality" that philosophers have proposed are really just rules describing default (non-reflexive) cognition. It can be perfectly rational for a reflexive cognizer to break these rules. The "normativity" of rationality is a reflection of a built-in feature of reflexive cognition -- when we detect violations of rationality, we have a tendency to desire to correct them. This is just another part of the descriptive theory of rationality. Although theories of rationality are descriptive, the structure of reflexive cognition gives philosophers, as human cognizers, privileged access to certain aspects of rational cognition. Philosophical theories of rationality are really scientific theories, based on inference to the best explanation, that take contingent introspective data as the evidence to be explained. Download paper in pdf form.

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**Vision, Knowledge, and the Mystery Link", coauthored with Iris Oved.**Almost final draft, to appear in*Philosophical Perspectives*vol. 19*.*It is argued that empirical data indicates that colors do not have characteristic looks that are the same for all people at all times. This creates a problem for many theories of perceptual knowledge. An examination of current computational theories of vision leads to an account of the visual image as a system of mental representations. This is used to develop an account of how epistemic cognition can produce beliefs on the basis of the visual image. The result is a sophisticated version of direct realism. Download paper in pdf form.

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**The need for an epistemology".***Proceedings of the 3rd International NASA Workshop on Planning and Scheduling for Space.*It is argued that we cannot build a sophisticated autonomous planetary rover just by implementing sophisticated planning algorithms. Planning must be based on information, and the agent must have the cognitive capability of acquiring new information about its environment. That requires the implementation of a sophisticated epistemology. Epistemological considerations indicate that the rover cannot be assumed to have a complete probability distribution at its disposal. Its planning must be based upon "thin" knowledge of probabilities, and that has important implications for what planning algorithms might be employed. Download paper in pdf form.

**"Defeasible reasoning with variable degrees of justification".***Artificial Intelligence***133**(2002), 233-282*.*The question addressed in this paper is how the degree of justification of a belief is determined. A conclusion may be supported by several different arguments, the arguments typically being defeasible, and there may also be arguments of varying strengths for defeaters for some of the supporting arguments. What is sought is a way of computing the "on sum" degree of justification of a conclusion in terms of the degrees of justification of all relevant premises and the strengths of all relevant reasons. I have in the past defended various principles pertaining to this problem. In this paper I reaffirm some of those principles but propose a significantly different final analysis. Specifically, I endorse the weakest link principle for the computation of argument strengths. According to this principle the degree of justification an argument confers on its conclusion in the absence of other relevant arguments is the minimum of the degrees of justification of its premises and the strengths of the reasons employed in the argument. I reaffirm my earlier rejection of the accrual of reasons, according to which two arguments for a conclusion can result in a higher degree of justification than either argument by itself. This paper diverges from my earlier theory mainly in its treatment of defeaters. First, it argues that defeaters that are too weak to defeat an inference outright may still diminish the strength of the conclusion. Second, in the past I have also denied that multiple defeaters can result in the defeat of an argument that is not defeated by any of the defeaters individually. In this paper I urge that there are compelling examples that support a limited version of this "collaborative" defeat. The need to accomodate diminishers and collaborative defeat has important consequences for the computation of degrees of justification. The paper proposes a characterization of degrees of justification that captures the various principles endorsed and constructs an algorithm for computing them. Download paper in pdf form.

**"OSCAR System Description".**This is a presentation at the Non-Monotonic Reasoning Workshop 2000. Download paper in pdf form.

**"Belief Revision and Epistemology".**Coauthored with Anthony Gillies.(*Synthese***122**(2000), 69-92)."Belief revision is the process of changing one's beliefs to reflect the acquisition of new information. There are two ways one might proceed in investigating rational belief revision. The postulational approach proposes abstract general principles purportedly governing the process--in effect, axiomatizing or partially axiomatizing belief revision. By contrast, the derivational approach tries to derive a theory of belief revison from a more concrete epistemological theory. A number of authors have favored the postulational approach. The best known such theory is the AGM theory originated by Alchourron, Gardenfors, and Makinson (1985). The purpose of this paper is to question the viability of that theory, and by implication to question the fruitfulness of the entire postulational approach. Our contention will be twofold. First, such theories of belief revision proceed at too high a level of abstraction, ignoring aspects of rational cognition without which it is impossible to formulate true principles of rational belief revision. A theory of belief revision should be driven by a more concrete epistemology. In the absence of an epistemological theory to generate a theory of belief revision, the latter will have be driven by nothing more than vague formal intuitions, and such intuitions are almost certain to get the details of belief revision wrong. Second, abstract theories of belief revision are not necessary anyway if we have a sufficiently detailed epistemological theory, because we can just apply the theory to see how to revise our beliefs. Of course, to serve this purpose the epistemological theory must be developed in sufficient detail to tell us precisely how belief revision should proceed in any given instance. Many epistemological theories are themselves too abstract to serve that purpose. The epistemological theory employed in this paper is that underlying the OSCAR architecture for rational agents (Pollock 1995). This theory is fully implemented in the AI system OSCAR and in any given case it makes a determinant decision about how an agent's beliefs should be revised." Postscript or pdf

**"Perceiving and Reasoning about a Changing World"**,*Computational Intelligence,*Volume 14, Number 4, 1998, 498-562. This is a revised version of 2/12/05. "A rational agent (artificial or otherwise) residing in a complex changing environment must gather information perceptually, update that information as the world changes, and combine that information with causal information to reason about the changing world. Using the system of defeasible reasoning that is incorporated into the OSCAR architecture for rational agents, a set of reason-schemas is proposed for enabling an agent to perform some of the requisite reasoning. Along the way, solutions are proposed for the Frame Problem, the Qualification Problem, and the Ramification Problem. The principles and reasoning described have all been implemented in OSCAR.". pdf

- Should we maximize expected value?, OSCAR Project technical report. "It is argued that cases of rational risk aversion force the abandonment of the view that rationality requires choosing actions that maximize expected value. It is proposed that plans should be the unit of decision-theoretic evaluation rather than individual actions." The source code for the simulation discussed in the paper can also be downloaded.

- Taking perception seriously. "A rational agent (artificial or otherwise) residing in a complex changing environment must gather information perceptually and update that information as the world changes. An agent designer must address two problems. First, perception need not be veridical?the world can be other than it appears. Second, perception is really a form of sampling. An agent cannot perceptually monitor the entire state of the world at all time. The best perception can do is provide the agent with images of small parts of the world at discrete times or over short time intervals, and it is up to the agent's cognitive faculties to make inferences from these to a coherent picture of the world. Using the system of defeasible reasoning that is incorporated into the OSCAR architecture for rational agents, a set of reason-schemas will be proposed for enabling an agent to perform some of the requisite reasoning. The principles and reasoning described have all been implemented in OSCAR." This is a longer version of a paper to appear in the proceedings of The First International Conference on Autonomous Agents.

- OSCAR-DSS, OSCAR Project technical report. "OSCAR-DSS is a generic decision support system based upon OSCAR--a general-purpose programmable architecture for rational agents. OSCAR-DSS is a partial implementation of that architecture, with some minor modifications to convert it to a decision support system that merely recommends actions rather than performing them. OSCAR-DSS has been further instantiated to produce OSCAR-MDA, a medical decision support system currently focused on emergency room medicine. The OSCAR architecture begins by distinguishing between epistemic cognition (cognition about what to believe) and practical cognition (cognition about what to do). The core of the OSCAR architecture consists of the system of epistemic cognition together with procedures for dealing with plans that have been adopted. The system of epistemic cognition is in turn based largely upon a general-purpose defeasible and deductive reasoner. The core is implemented directly in LISP. The bulk of the system of practical cognition is then implemented indirectly in the core by giving the system of epistemic cognition the ability to reason about plan adoptability."

- Reason in a Changing World,
*International Conference on Formal and Applied Practical Reasoning*, Bonn, Germany, 1996. This is the version of the paper presented at the conference, but it is a significant revision of the paper appearing in the Proceedings.

- OSCAR-MDA: an artificially intelligent advisor for emergency room medicine,
*AAAI Spring Symposium on AI in Medicine*, 1996.

- Emergency Room Reasoning, technical report on the current state of OSCAR-MDA, 1996.

- Rational Agency in OSCAR,
*AAAI Fall Symposium on Rational Agency*, 1995.

- OSCAR--a general purpose defeasible reasoner,
*Journal of Applied Non-Classical Logics***6**, (1996), 89-113.

- Practical Reasoning in OSCAR,
*Philosophical Perspectives***9**, (1995), 15-48.

- The phylogeny of rationality,
*Cognitive Science***17**, (1993), 563-588.

- New foundations for practical reasoning,
*Minds and Machines***2**, (1992), 113-144.