Doyle J. (1992) Rationality and its roles in reasoning, Computational Intelligence 8(2), pp. 376--409.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....Savage [77] who simultaneously axiomatized utility and subjective behavior) This is also in keeping with a large (and growing) body of work within artificial intelligence that attributes rationality (or explores the consequences of attributing rationality) to autonomous agents. See, for example, [46, 25, 70, 12, 42, 34]. 1.3 Overview of this Article In this article we present a method for reaching consensus based on the Clarke Tax mechanism [6, 7] CTm) and consider how this mechanism could be used among rational automated agents. Parts of this work have appeared previously in [18, 22, 20, 19] In Section ....

Jon Doyle. Rationality and its role in reasoning. Computational Intelligence, 8(2):376-- 409, May 1992.

....14, 19, 21, 22] 2 The problem of deliberation cost has been widely discussed in artificial intelligence, economics, engineering and philosophy. In artificial intelligence in particular, researchers have proposed a number of meta level architectures to control the cost of base level reasoning [4, 7, 9, 12, 18]. One promising approach is to use anytime [3] or flexible [10] algorithms, which allow the execution time to be specified, either as a parameter or by an interrupt, and exhibit a time quality tradeoff defined by a performance profile. They provide a simple means by which a system can control its ....

J. Doyle, Rationality and its roles in reasoning, In Proceedings of the Eighth National Conference on Artificial Intelligence, Boston, Massachusetts (1990) 1093--1100.

....cost [1, 4, 16, 21, 28, 31, 32] The problem of deliberation cost has been widely discussed in artificial intelligence, economics and philosophy. In artificial intelligence in particular, researchers have proposed a number of meta level architectures to control the cost of base level reasoning [5, 6, 8, 12, 16, 27]. One promising approach is to use anytime [7] orfiexible [14] algorithms, which allow the execution time to be specified, either as a parameter or by an interrupt, and exhibit a time quality tradeoff defined by a performance profile. They provide a simple means by which a system can control its ....

J. Doyle, Rationality and its roles in reasoning, In Proceedings of the Eighth National Conference on Artificial Intelligence, Boston, Massachusetts (1990) 1093-1 tOO.

....by whether information relevant to the task is distributed between the agents or primarily known by one agent [Walker and Whittaker, 1990; Guinn, 1993] 14 10 process. Desires: Agents may have different types of desires but here I assume that their only desire is to maximize utility[Doyle, 1992]. Intention Deliberation: decides which of a set of options to pursue (by an evaluation based on desires such as maximizing utility) Attention Working memory ( WM) the limited attention module constrains working memory and the retrieval of current beliefs and intentions that are used by ....

....shows, incoming messages about intentions and beliefs are subject to intention or belief deliberation. This provides the basis for abandoning the NO AUTONOMY ASSUMPTION while specifying why an agent would ac cept or reject another agent s proposal (see also [Galliers, 1989; Galliers, 1991b; Doyle, 1992] Agents evaluate assertions and proposals from other agents by assessing the support for assertions and the warrants for proposals. Finally, as figure 3 shows, this evaluation takes place under constraints of limited working memory, since the beliefs that can serve as supports or warrants must ....

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Jon Doyle. Rationality and its roles in reasoning. Computational Intelligence, November 1992.

....the derived performance function to make predictions and to learn how to optimize agent dialogue behavior. In Section 5 we conclude and suggest future work. 2 PARADISE: A Framework for Deriving Performance Models for Dialogue PARADISE uses methods from decision theory [Keeney and Raiffa, 1976; Doyle, 1992] to combine a disparate set of performance measures (i.e. user satisfaction, task success, and dialogue cost, all of which have been previously noted in the literature) into a single performance evaluation function. The use of decision theory requires a specification of both the objectives of ....

Jon Doyle. Rationality and its roles in reasoning. Computational Intelligence, 8(2):376--409, 1992.

....deadlines. Ever since then, the concept of imprecise computation has been applied to solve several diverse problems [6, 7, 8, 9] The idea of anytime algorithms is also similar to the notion of rationality in automated reasoning and search investigated by Russel et al. in [10, 11] Doyle in [12] and D Ambrosio in [13] Real time computational tasks that are anytime algorithms, prove to be useful in the design of real time systems. The property of anytime algorithms to trade computational time for decision quality results in optimal performance of real time systems [14, 15, 16] ....

J. Doyle. Rationality and Its Roles in Reasoning. In Proceedings of the Eight National Conference on Arti cial Intelligence, pages 1093-1100, Menlo Park, California, 1990. American Association for Arti cial Intelligence.

....Y axis is F0, X axis is time. 3.2. 2 Deliberation Rejections Deliberation rejections are a way of rejecting proposals that rely on default inferences about the deliberative processes by which an agent evaluates the potential benefit or utility of pursuing a course of action [Bratman et al. 1988; Doyle, 1992] Deliberation inferences are the action parallel of epistemic inferences. Where epistemic inferences are concerned with belief and truth, deliberative inferences involve intentions and the desirability, utility or expected payoff of one potential intention when compared with another. Rejections ....

.... some shared goal of finding a solution to the caller s problem [Grosz and Sidner, 1990; Walker and Whittaker, 1990; Traum, 1994; Chu Carrol and Carberry, 1994] Given this shared goal, they then deliberate about which intentions to adopt as means for satisfying that goal [Bratman et al. 1988; Doyle, 1992] The degreei in the consequent of 42 is defined by possible courses of action, or alternate potential plans that a conversant may consider, and the degree of the utility of the proposed course of action in comparison with other alternate means. These inference rules are default inference rules ....

Jon Doyle. Rationality and its roles in reasoning. Computational Intelligence, 8(2):376-409, 1992.

.... since then, the concept of imprecise computation has been applied to solve several diverse problems [YAT02, MLFL94, HC95, LKL01] The idea of anytime algorithms is also similar to the notion of rationality in automated reasoning and search investigated by Russel et al. in [RW89, RW91] Doyle in [Doy90] and D Ambrosio in [D A89] Real time computational tasks that are anytime algorithms, prove to be useful in the design of real time systems. The property of anytime algorithms to trade computational time for decision quality results in optimal performance of real time systems [DB88, Hor87, ....

J. Doyle. Rationality and Its Roles in Reasoning. In Proceedings of the Eight National Conference on Artificial Intelligence, pages 1093--1100, Menlo Park, California, 1990. American Association for Artificial Intelligence.

....itself is normally unavoidable, leading to a deliberative agent. Since the resources available to the agent (in terms of computational power, memory, etc. are bounded, the resulting behavior may be imperfect hence the distinction between bounded and perfect rationality [Good, 1971; Simon, 1982; Doyle, 1990; Russell and Wefald, 1991] Bounded rationality is a desired property of intelligent agents since it provides a good evaluation criteria and since it establishes a formal framework to analyze agents. This paper addresses several fundamental questions related to models of bounded rationality: ....

J. Doyle. Rationality and its roles in rea- soning. Proceedings of the Eighth National Conference on Artificial Intelligence, pp. 1093-1100, Boston, Massachusetts, 1990.

.... heuristic strategies for achieving DELIBEIAq IoN Based intentions[9] 2 Deliberation DELIBERATION as a component of a theory of intention in discourse is functionally related to the theory of economic rationality, which in recent years has augmented the INFORMATION based (logical) view of action [3]. DELIBERATION iS the process by which an agent explicitly or implicitly evaluates a set of alternates in order to decide what s he wants to believe and what course of action s he wants to pursueJ Thus agents deliberate about whether as well as how to revise their beliefs and intentions as they ....

Jou Doyle. Rationality and its roles in reasoning. Computational Intelligence, November 1992.

....use MOTIVATION for discourse planning The effect of presentational relations is always to increase H s belief, de sire, or intention. Thus we will need (in the case of MOTIVATION) some sort of representation of degree of desire. In a first attempt at using MOTIVATION, we will use utility theory [6] and simply associate utilities with proposed actions. Under this view, an agent s strength of desire to perform an action is the utility he or she believes performing the action will yield, where utility is a quantifiable variable. In section 6 we will discuss the limitations of this approach. ....

....agent kim option 45: put act (agent bill green couch room 1) 4: KIM: No, instead let s put the purple couch in the study. reject agent ldm agent bill option 56: put act (agentldm purple couch robin l) On receiving a proposal, an agent deliberates whether to ccrT or R. CT the proposal [6]. Each furniture The generation of the gloss was not a focus of this study and was done via adhoc methods. item has a value that contributes to an evaluation of the final plan. The values on the furniture items range from 10 to 56, and both agents furniture items range over these values. Agents ....

J. Doyle. Rationality and its roles in reasoning. Computational Intelligence, November 1992.

....address what we regard as the basic features of active logics and how they differ from other approaches to reasoning. Chief among these are: limited reasoning, temporal reasoning, meta reasoning, real time planning, reasoning engines, indexicality, and belief revision. 1.2. 1 Limited Reasoning Doyle [ Doyle, 1992 ] discusses the complementary roles that the economics theory of rationality and mathematical logic play in the development of rational automated agents. In particular Doyle explores some major limitations of agents that influence their rationality. Active logic was developed to overcome such ....

J. Doyle. Rationality and its roles in reasoning. Computational Intelligence, 8(2):376--409, 1992.

....Answers to these basic questions are needed if robust and reliable agent based systems are to be developed. Designers want their agents to be rational and to do the right thing [16] To this end, a major strand of research has adopted the economic viewpoint and looked at self interested agents [7] that consider what action to take solely in terms of its worth to themselves. However, this is only part of the story. When an agent is situated in a multi agent context, its actions can have non local effects. For example, the actions of different agents can conflict or result in duplication of ....

J. Doyle. Rationality and its role in reasoning. Computational Intelligence, 8(3):376--409, 1992.

....Not much work has been done based on results from the area of multi criteria decision aid (MCDA) cf. 3] for handling such problems. Other aspects of decision theory, however, have influenced the area of MASs (cf. 4] partly as a result of philosophical aspects of agent rationality [5], and partly because of interest in extending the principle of maximising the expected utility in efficient real life applications [6] The viewpoint taken in this paper is that MCDA is well suited for the treatment of systems of cooperating agents. A typical multi criteria decision model is ....

J. Doyle, Rationality and its Roles in Reasoning, Computational Intelligence 8:2 (1992) 376-- 409.

....and emotions are useful in designing competent arti cial agents that are to operate within complex uncertain environments populated by other arti cial and human agents. Our work draws on and combines an emerging technology of rational agent design from arti cial intelligence on the one hand [7, 9, 28, 34], with research on human emotions in cognitive science and psychology on the other hand [8, 11, 15, 16, 17, 20, 25, 30, 32, 31] Our work accepts the decision theoretic paradigm of rationality, according to which a rational agent should behave so as to maximize the expected utility of its actions ....

.... research on human emotions in cognitive science and psychology on the other hand [8, 11, 15, 16, 17, 20, 25, 30, 32, 31] Our work accepts the decision theoretic paradigm of rationality, according to which a rational agent should behave so as to maximize the expected utility of its actions (see [3, 9, 28] and other references therein) The expected utilities of the alternative courses of action are computed based on their possible consequences, the desirability of these consequences to the agent, 1 and the probabilities with which these consequences are thought by the agent to obtain. 2 We aim ....

[Article contains additional citation context not shown here]

Jon Doyle. Rationality and its role in reasoning. Computational Intelligence, 8:376-409, 1992.

....and plans using mathematical logic, and attempted to define a prescriptive theory of action using the same tool. An alternative approach, placing more emphasis on decision theoretic considerations (so as to ensure reasoned choice among alternative strategies) is represented, e.g. in [FS] Hz] [Do], DB] De] and the book [RW] Computers playing classical games are already a field in itself with special purpose computers and specialized periodicals. Two nontraditional games that may be worth mentioning are Diplomacy, for which a program was developed [KEL] which negotiates with the other ....

J. Doyle, Rationality and its Roles in Reasoning (Extended Abstract), in Proceedings of the Eighth National Conference on Artificial Intelligence (1990) 1093 - 1100.

....is, therefore, with the decision making apparatus that they should use. Traditionally, designers have sought to make their agents rational so that they can do the right thing [1] To this end, a major strand of research has adopted an economic viewpoint and looked at self interested agents [2] that consider what action to take solely in terms of its worth to themselves. However, this is only part of the story. When an agent is situated in a social context, its actions can often have nonlocal effects. For example, the actions of different agents can conflict or result in duplication of ....

J. Doyle, "Rationality and its role in reasoning," Computat. Intell., vol. 8, no. 3, pp. 376--409, 1992.

....non constrained approach agents have been assumed free : They are not subject to any kind of social laws. The only requirement for an agent to do something is that he must have the corresponding capability. However, several researchers have pointed out recently some of the RCT s drawbacks (e.g. [6, 16, 17]) namely: 1. In real life, dynamic domains agents usually do not have enough information or time to perform complex, optimal utility calculus. An agent would not know all the alternatives, would not know the exact outcome of each, and would not have a complete preference order for those ....

J. Doyle. Rationality and its role in reasoning. Computational Intelligence, 8:376--409, 1992.

....formal game theory approach provides clear analyses of various situations and precise results concerning the strategy a negotiator should choose. However, it requires making restrictive assumptions that are unacceptable to the first group. Following Genesereth, Ginsberg, Rosenschein and Doyle, [18, 44, 8, 9], we propose the use of game theoretic techniques for Artificial Intelligence purposes. We propose to develop a strategic model of negotiation that can serve as the basis for building efficient automated negotiators. The formal game theory approach is also divided into two central sub approaches ....

J. Doyle. Rationality and its role in reasoning. In Proc. of AAAI-90, Boston, MA, 1990. Invited Talk.

....agent with a decision mechanism based on some given set of preferences. Structures of symbolic goals provide the agents with a good framework for planning, when the world is perfectly controlled by the agent and the effects of all the operators are known completely and with certainty to the agent [33, 17]. Symbolic goals are easily communicated, they guide the search for alternative plans and the projection process, and they also solve the horizon problem (see [33] for detailed discussion. However, symbolic goals do not give any information about the relative merits of different desirable ....

J. Doyle. Rationality and its role in reasoning. Computational Intelligence, 8(2):376-- 409, 1992.

....describes a Bayesian method for learning class probability trees, based on previous work on learning decision trees (e.g. Quinlan, 1986] but using Bayesian, rather than information theoretic, techniques for splitting trees and averaging predictions over multiple trees. 2.3. 4 Planning to Learn Doyle s [1990] definition of learning is interpreting experience by making rational changes of mental state or expectation. Being rational means deciding whether and what to learn based on the expected utility gain of doing so (due to the increased accuracy of predictions) and the associated cost of learning, ....

Jon Doyle. Rationality and its roles in reasoning. In AAAI, pages 1093--1100, 1990.

....for expressing time points time variables which can be quantified (as in, for example, 53] Thus, it is easier to express in our meta logic axioms such as the ones in Section 4 on focus of attention. Also, our agents can express time explicitly in their reasoning about their knowledge. Doyle [14] discusses the complementary roles that the economics theory of rationality and mathematical logic play in the development of rational automated agents. In particular Doyle explores some major limitations of agents that influence their rationality. Our approach was developed to overcome such ....

J. Doyle. Rationality and its roles in reasoning. Computational Intelligence, 8(2):376-- 409, 1992.

....into representing and reasoning about multi agent encounters. We have presented two alternative formalisms for enabling an automated agent to discover focal points, one based on step logic and the other on decision theory. Each has certain advantages and disadvantages. As Doyle points out [17, 16], logic and decision theory are not competing theories, but instead are two complementary parts of the solution. It thus makes sense to consider how each might be used to treat a difficult new problem in knowledge representation, to consider their strengths and weaknesses, and ultimately, ....

J. Doyle. Rationality and its role in reasoning. Computational Intelligence, 8(2):376--409, 1992.

....out to be very useful in designing competent artificial agents that should operate within complex uncertain environments populated by other artificial and human agents. Our approach draws on and combines an emerging technology of rational agent design of Artificial Intelligence on the one hand [5, 7, 25, 27, 31], with research on human emotions in cognitive science and psychology on the other hand [6, 12, 13, 14, 17, 22, 26, 29, 28] Our work accepts the decision theoretic paradigm of rationality, according to which rational agent should behave so as to maximize the expected utility of its actions (see ....

.... with research on human emotions in cognitive science and psychology on the other hand [6, 12, 13, 14, 17, 22, 26, 29, 28] Our work accepts the decision theoretic paradigm of rationality, according to which rational agent should behave so as to maximize the expected utility of its actions (see [7, 25] and other references therein) The expected utilities of the alternative courses of action are computed based on their possible consequences, the desirability of these consequences to the agent, 1 and the probabilities with which these consequences are thought by the agent to obtain. 2 The ....

Jon Doyle. Rationality and its role in reasoning. Computational Intelligence, 8:376--409, 1992. 9

....analysis of a perceived 16 scene within reasonable time constraints. Some aspects of a scene are more relevant than others, and it would be irrational to waste time and computational resources to detect true but useless details. This is a typical problem of traditional symbolic models: Doyle [27] and Cherniak [22] stress the fact that, in order to avoid the proliferation of insignificant true conclusions, the aims and the purposes of an agent must be taken into account in the modeling of inferential activities. In modeling perception, these problems can be faced by taking into account the ....

J. Doyle. Rationality and its roles in reasoning. In Proc. AAAI-90, pages 1093--1100, 1990.

....based on knowledge it has about itself and others, without relying on protocols or conventions. In our work, we use the normative decision theoretic paradigm of rational decision making under uncertainty, according to which an agent should make decisions so as to maximize its expected utility [17, 20, 23, 32, 40, 68]. Decision theory is applicable to agents interacting with other agents because of uncertainty: The abilities, sensing capabilities, beliefs, goals, preferences, and intentions of other agents clearly are not directly observable and usually are not known with certainty. In decision theory, ....

.... its purposes in the current situation) to others [78] Our ability to represent agents preferences over actions as payoffs follows from the axioms of utility theory, which postulate that ordinal preferences among actions in the current situation can be represented as cardinal, numeric values (see [13, 20] for details) We represent R i s payoff associated with a joint action (a 1 k ; Delta Delta Delta ; a i m ; Delta Delta Delta ; a n l ) as u R i a 1 k Delta Delta Deltaa i m Delta Delta Deltaa n l . We now define the recursive model structure of agent R i , RMSR i , as ....

Jon Doyle. Rationality and its role in reasoning. Computational Intelligence, 8:376--409, 1992.

....decisions. A similar technique, termed flexible computation, was introduced by Horvitz (1990, 1987) to solve time critical decision problems. This line of work is also closely related to the notion of limited rationality in automated reasoning and search (Russell and Wefald 1991, 1989; Doyle 1990; D Ambrosio 1989) Within the systems community, a similar idea termed imprecise computation was developed by Jane Liu and others (1991) What is common to these research efforts is the recognition that the computation time needed to compute precise or optimal solutions will typically reduce the ....

Doyle, J. 1990. Rationality and Its Roles in Reasoning.

....are needed if robust and reliable agent based systems are to be developed. To build successful applications, designers need their agents to be rational and to do the right thing [20] To this end, a major strand of research has adopted the economic viewpoint and looked at self interested agents [10] that consider what action to take solely in terms of its worth to themselves. However, this is only part of the story. When an agent is situated in a multi agent context, its actions can often have non local effects. For example, the actions of different agents can conflict or result in ....

J. Doyle. Rationality and its role in reasoning. Computational Intelligence, 8(3):376--409, 1992.

....assumptions and their degrees of entrenchment are explicitly given to the system (which then reasons with them) They are thus the only data which can form a basis of decision between conflicting derived beliefs. Compare this to decision theory as employed in, for example, economics. Recently, Doyle 92b] has strongly advocated the use of decision theory in AI. He mentions some connections between decision theory and belief revision, but regrets that little has been done so far in order to integrate the two. The system presented here hopes to give ideas as to how to fill this gap. A decision is ....

Jon Doyle, Rationality and its Roles in Reasoning, in: Computational Intelligence, 8, 1992, 376--409

....what we regard as the basic features of active logics and how they differ from other approaches to reasoning. Chief among these are: limited reasoning, temporal reasoning, meta reasoning, real time planning, reasoning engines, indexicality, and belief revision. 1.2. 1 Limited Reasoning Doyle [ 17 ] discusses the complementary roles that the economics theory of rationality and mathematical logic play in the development of rational automated agents. In particular Doyle explores some major limitations of agents that influence their rationality. Active logic was developed to overcome such ....

....particular facts at step 4. 45 The reasoning continues from step to step. Note that at step 11, wiseman #1 has been able to deduce that wiseman #2 knows that if wiseman #1 s card is black, then his is white. From this step on, we essentially have the Two wise men problem. See [ 23 ] In step 17 wiseman #1 is finally able to deduce that his card is white. We see that active logic is a useful vehicle for formulating and solving a problem of this kind in which the time that something occurs is important. wiseman #1 does indeed determine if wiseman #2 or wiseman #3 knew the color of his ....

J. Doyle. Rationality and its roles in reasoning. Computational Intelligence, 8(2):376--409, 1992.

....of computing an appropriate action to perform [12] p.5. Although DRIPS is based on decision theory, it is in fact a computational model based on the notion of utility, which exploits various techniques to account for probability and uncertainty: these are important factors that, as [9] has noticed, tend to be underestimated in logic based formalizations. However, DRIPS deals only with single agents plans and individual utility. So its basic planning mechanism was extended to build the planning architecture which satisfies our definition of cooperation to a shared plan. 3 The ....

....plan has already been given and the planning mechanism is exploited by an agent to find which way of executing its part in the plan maximizes the utility of the group. Moreover, the goal adoption mechanism accounts for the new goals that arise during the execution of the plan. 8 Conclusions [9] has highlighted the role that the economical theories of rationality, as decision theory is, can have in AI, notwithstanding the many restrictive assumptions that it is necessary to make in order to exploit this kind of theories. In this paper, we show how a decision theoretic approach to ....

J. Doyle. Rationality and its roles in reasoning. Computational Intelligence, 8:376--409, 1992.

....A B (ff 1 : ff n ) 2. 8ff i (MB A B (Contribute ff i (Achieve A B Goal) 3. MB A B (Max Utility (ff 1 : ff n ) Achieve A B Goal) The use of the Max Utility constraint in the formulation of collaborative planning incorporates the role of deliberation in planning future actions[8]. A is achieved by a cycle in which: 1) individual agents perform means end reasoning about options in the domain; 2) individual agents deliberate about which options are preferable; 3) then agents make to other agents, based on the options identified in a reasoning cycle, about actions that ....

Jon Doyle. Rationality and its roles in reasoning. Computational Intelligence, November 1992.

....the paper uses simplified domains with hypothetical data throughout. Section 2 describes PARADISE s performance model, and Section 3 discusses its generality, before concluding in Section 4. 2 A Performance Model for Dialogue PARADISE uses methods from decision theory (Keeney and Raiffa, 1976; Doyle, 1992) to combine a disparate set of performance measures (i.e. user satisfaction, task success, and dialogue cost, all of which have been previously noted in the literature) into a single performance evaluation function. The use of decision theory requires a specification of both the objectives of the ....

Doyle, Jon. 1992. Rationality and its roles in reasoning.

....factors. The experimental results in section 4 show that effective strategies to support deliberation are determined by both cognitive and task variables. 2 Deliberation in Discourse Deliberation is the process by which an agent decides what to believe and what to do [ Galliers, 1991; Doyle, 1992 ] One strategy that supports deliberation is the Explicit Warrant strategy, as in 1. The warrant in 1b can be used by the hearer in deliberating whether to accept or reject the speaker s proposal in 1a. 1 An analysis of proposals in a corpus of 55 problemsolving dialogues shows that ....

....agent bill agent kim option 45: put act (agentbill green couch room 1) 4: KIM: No, instead let s put in the purple couch. reject agent kim agent bill option 56: put act (agent kim purple couch room 1) On receiving a proposal, an agent deliberates whether to accept or reject the proposal [ Doyle, 1992 ] As potential warrants to support deliberation, and to provide a way of objectively evaluating agents performance, each piece of furniture has a score. The score propositions for all the pieces of furniture are stored in both agents memories at the beginning of the dialogue. Agents reject a ....

Jon Doyle. Rationality and its roles in reasoning. Computational Intelligence, November 1992.

....We need to investigate how to integrate these theories in useful ways that recognize that meaning, possibility, utility, and probability must all be evaluated with respect to changing purposes and circumstances. 31 Doyle Acknowledgments This paper is an extended version of an invited talk (Doyle, 1990) presented at AAAI90. I thank Ramesh Patil, Peter Szolovits, and Michael Wellman for reading drafts, Rich Thomason for lending me some of his notes, Tom Dean, Othar Hansson, Eric Horvitz, Barton Lipman, Andrew Mayer, Stuart Russell, Joseph Schatz, and David Smith for valuable discussions, and the ....

Doyle, J. 1990. Rationality and its roles in reasoning (extended abstract). In Proceedings of the Eighth National Conference on Artificial Intelligence, pp. 1093--1100.

.... 9, 12] has been complemented in recent years by studies with Michael Wellman (now on the faculty at University of Michigan) of qualitative representations of preference information [52, 14, 53, 16] by studies of the use of economic mechanisms in controlling distributed reasoning and activities [7, 11, 12], and by work on constructing ontologies for plans and the process of planning. The ontology research has been conducted in conjunction with the ARPI Planning Ontology Construction Group. We are engaged in a number of projects that exploit the revolutionary capabilities of the World Wide Web (W3) ....

J. Doyle. Rationality and its roles in reasoning. Computational Intelligence, 8(2):376--409, 1992.

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Doyle J. (1992) Rationality and its roles in reasoning, Computational Intelligence 8(2), pp. 376--409.

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Doyle J. (1992) Rationality and its roles in reasoning, Computational Intelligence 8(2), pp. 376--409.

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J. Doyle. Rationality and Its Roles in Reasoning. Computational Intelligence, 8(2):376-409, 1992.

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Doyle, J. 1990. Rationality and it roles in reasoning. In AAAI, 1093--1100.

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J. Doyle, Rationality and Its Roles in Reasoning. Computational Intelligence, Vol. 8, 1992.

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J. Doyle. Rationality and its roles in reasoning. Computational Intelligence, 8(2):376--409, 1992.

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J. Doyle. Rationality and its role in reasoning. Computational Intelligence, 8:376--409, 1992.

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J. Doyle. Rationality and its roles in reasoning. Computational Intelligence, 8(2):376 -- 409, 1992.

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Jon Doyle. Rationality and its roles in reasoning. Computational Intelligence, 8(2):376-- 409, May 1992. 47

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J. Doyle. Rationality and its roles in reasoning. In Proceedings of the Eighth National Conference on Artificial Intelligence, pp. 1093--1100, Boston, Massachusetts, 1990.

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Doyle, J., "Rationality and its Roles in Reasoning", Computational Intelligence, Vol. 8, No. 2, pp. 376-409, 1992.

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Doyle, J., "Rationality and its Roles in Reasoning", Computational Intelligence, Vol. 8, No. 2, pp. 376-409, 1992.

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Jon Doyle. 1992. Rationality and its roles in reasoning. Computational Intelligence, November.

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