Above are papers related to the following paper: [Paper_ID] Paper #504. 	[Title] Constructive Belief and Rational Representation [Abstract] It is commonplace in artificial intelligence to divide an agent's explicit beliefs into two parts: the beliefs explicitly represented or manifest in memory, and the implicitly represented or constructive beliefs that are repeatedly reconstructed when needed rather than memorized. Many theories of knowledge view the relation between manifest and constructive beliefs as a logical relation, with the manifest beliefs representing the constructive beliefs through a logic of belief. This view, however, limits the ability of a theory to treat incomplete or inconsistent sets of beliefs in useful ways. We argue that a more illuminating view is that belief is the result of rational representation. In this theory, the agent obtains its constructive beliefs by using its manifest beliefs and preferences to rationally (in the sense of decision theory) choose the most useful conclusions indicated by the manifest beliefs. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153024. 	[Title]  [Abstract] This reproduces a report submitted to Rome Laboratory on October 27, 1994. c flCopyright 1994 by Jon Doyle. All rights reserved. Freely available via http://www.medg.lcs.mit.edu/doyle. Final Report on Rational Distributed Reason Maintenance for Abstract Efficiency dictates that plans for large-scale distributed activities be revised incrementally, with parts of plans being revised only if the expected utility of identifying and revising the sub-plans improve on the expected utility of using the original plan. The problems of identifying and reconsidering the subplans affected by changed circumstances or goals are closely related to the problems of revising beliefs as new or changed information is gained. But traditional techniques of reason maintenance|the standard method for belief revision|choose revisions arbitrarily and enforce global notions of consistency and groundedness which may mean reconsidering all beliefs or plan elements at each step. We develop revision methods aiming to revise only those beliefs and plans worth revising, and to tolerate incoherence and ungroundedness when these are judged less detrimental than a costly revision effort. We use an artificial market economy in planning and revision tasks to arrive at overall judgments of worth, and present a representation for qualitative preferences that permits capture of common forms of dominance information. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #89416. 	[Title] Rational Belief Revision (Preliminary Report) [Abstract] Theories of rational belief revision recently proposed by Alchourron, Gardenfors, Makin-son, and Nebel illuminate many important issues but impose unnecessarily strong standards for correct revisions and make strong assumptions about what information is available to guide revisions. We reconstruct these theories according to an economic standard of rationality in which preferences are used to select among alternative possible revisions. By permitting multiple partial specifications of preferences in ways closely related to preference-based nonmonotonic logics, the reconstructed theory employs information closer to that available in practice and offers more flexible ways of selecting revisions. We formally compare this new conception of rational belief revision with the original theories, adapt results about universal default theories to prove that there is unlikely to be any universal method of rational belief revision, and examine formally how different limitations on rationality affect belief revision. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1112650. 	[Title] On Decision-Theoretic Foundations for Defaults [Abstract] In recent years, considerable effort has gone into understanding default reasoning. Most of this effort concentrated on the question of entailment, i.e., what conclusions are warranted by a knowledge-base of defaults. Surprisingly, few works formally examine the general role of defaults. We argue that an examination of this role is necessary in order to understand defaults, and suggest a concrete role for defaults: Defaults simplify our decision-making process, allowing us to make fast, approximately optimal decisions by ignoring certain possible states. In order to formalize this approach, we examine decision making in the framework of decision theory. We use probability and utility to measure the impact of possible states on the decision-making process. We accept a default if it ignores states with small impact according to our measure. We motivate our choice of measures and show that the resulting formalization of defaults satisfies desired properties of defaults, namely cumulative reasoning. Finally, we compare our approach with Poole's decision-theoretic defaults, and show how both can be combined to form an attractive framework for reasoning about decisions. We make numerous assumptions each day: the car will start, the road will not be blocked, there will be heavy traffic at 5pm, etc. Many of these assumptions are defeasible; we are willing to retract them given sufficient evidence. Humans naturally state defaults and draw conclusions from default information. Hence, defaults seem to play an important part in common-sense reasoning. To use such statements, however, we need a formal understanding of what defaults represent and what conclusions they admit. The problem of default entailment|roughly, what conclusions we should draw from a knowledge-base of defaults|has attracted a great deal of attention. Many researchers attempt to find "context-free" patterns of default reasoning (e.g., [ Kraus et al., 1990 ] ). As this research shows, much can be done in this approach. We claim, however, that the utility of this approach is limited; to gain a better understanding of defaults, we need to understand in what situations we should be willing to state a default. Our main thesis is that an investigation of defaults should elaborate their role in the behavior of the reasoning agent. This role should allow us to examine when a default is appropriate in terms of its implications on the agent's overall performance. In this paper, we suggest a particular role for defaults and show how this role allows us to provide a semantics for defaults. Of course, we do not claim that this is the only role defaults can play. In many applications, the end result of reasoning is a choice of actions. Usually, this choice is not optimal; there is too much uncertainty about the state of the world and the effects of actions to allow for an examination of all possibilities. We suggest that one role of defaults lies in simplifying our decision-making process by stating assumptions that reduce the space of examined possibilities. More precisely, we suggest that a default ' ! is a license to ignore : situations when our knowledge amounts to '. One particular suggestion that can be understood in this light is *-semantics [ Pearl, 1989 ] . In *-semantics, we accept a default ' ! if given the knowledge ', the probability of : is very small. This small probability of the :' states gives us a license to ignore them. Although probability plays an important part in our decisions, we claim that we should also examine the utility of our actions. For example, while most people think that it is highly unlikely that they will die next year, they also believe that they should not accept this as a default assumption in the context of a decision as to whether or not to buy life insurance. In this context, the stakes are too high to ignore this outcome, even though it is unlikely. We suggest that the license to ignore a set should be given based on its impact on our decision. To paraphrase this view, we should accept Bird ! Fly if assuming that the bird flies cannot get us into too much trouble. To formalize our intuitions we examine decision-making in the framework of decision theory [ Luce and Raiffa, 1957 ] . Decision theory represents a decision problem using several components: a set of possible states, a probability measure over these sets, and a utility function that assigns to each action and state a numerical value. fl To appear in IJCAI'95. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #137130. 	[Title] On the Semantics of Belief Revision Systems [Abstract] We consider belief revision operators that satisfy the Alchourron-Gardenfors-Makinson postulates, and present an epistemic logic in which, for any such revision operator, the result of a revision can be described by a sentence in the logic. In our logic, the fact that the agent's set of beliefs is is represented by the sentence O, where O is Levesque's `only know' operator. Intuitively, O is read as ` is all that is believed.' The fact that the agent believes is represented by the sentence B , read in the usual way as ` is believed'. The connective represents update as defined by Katsuno and Mendelzon. The revised beliefs are represented by the sentence O B . We show that for every revision operator that satisfies the AGM postulates, there is a model for our epistemic logic such that the beliefs implied by the sentence O B in this model correspond exactly to the sentences implied by the theory that results from revising by . This means that reasoning about changes in the agent's beliefs reduces to model checking of certain epistemic sentences. The negative result in the paper is that this type of formal account of revision cannot be extended to the situation where the agent is able to reason about its beliefs. A fully introspective agent cannot use our construction to reason about the results of its own revisions, on pain of triviality. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106546. 	[Title] Toward Rational Planning and Replanning Rational Reason Maintenance, Reasoning Economies, and Qualitative Preferences formal notions [Abstract] Efficiency dictates that plans for large-scale distributed activities be revised incrementally, with parts of plans being revised only if the expected utility of identifying and revising the subplans improves on the expected utility of using the original plan. The problems of identifying and reconsidering the subplans affected by changed circumstances or goals are closely related to the problems of revising beliefs as new or changed information is gained. But traditional techniques of reason maintenance|the standard method for belief revision|choose revisions arbitrarily and enforce global notions of consistency and groundedness which may mean reconsidering all beliefs or plan elements at each step. To address these problems, we developed (1) revision methods aimed at revising only those beliefs and plans worth revising, and tolerating incoherence and ungroundedness when these are judged less detrimental than a costly revision effort, (2) an artificial market economy in planning and revision tasks for arriving at overall judgments of worth, and (3) a representation for qualitative preferences that permits capture of common forms of dominance information. We view the activities of intelligent agents as stemming from interleaved or simultaneous planning, replanning, execution, and observation subactivities. In this model of the plan construction process, the agents continually evaluate and revise their plans in light of what happens in the world. Planning is necessary for the organization of large-scale activities because decisions about actions to be taken in the future have direct impact on what should be done in the shorter term. But even if well-constructed, the value of a plan decays as changing circumstances, resources, information, or objectives render the original course of action inappropriate. When changes occur before or during execution of the plan, it may be necessary to construct a new plan by starting from scratch or by revising a previous plan. only the portions of the plan actually affected by the changes. Given the information accrued during plan execution, which remaining parts of the original plan should be salvaged and in what ways should other parts be changed? Incremental replanning first involves localizing the potential changes or conflicts by identifying the subset of the extant beliefs and plans in which they occur. It then involves choosing which of the identified beliefs and plans to keep and which to change. For greatest efficiency, the choices of what portion of the plan to revise and how to revise it should be based on coherent expectations about and preferences among the consequences of different alternatives so as to be rational in the sense of decision theory (Savage 1972). Our work toward mechanizing rational planning and replanning has focussed on four main issues: This paper focusses on the latter three issues; for our approach to the first, see (Doyle 1988; 1992). Replanning in an incremental and local manner requires that the planning procedures routinely identify the assumptions made during planning and connect plan elements with these assumptions, so that replan-ning may seek to change only those portions of a plan dependent upon assumptions brought into question by new information. Consequently, the problem of revising plans to account for changed conditions has much Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #65074. 	[Title] Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions. [Abstract] We describe a ranked-model semantics for if-then rules admitting exceptions, which provides a coherent framework for many facets of evidential and causal reasoning. Rule priorities are automatically extracted form the knowledge base to facilitate the construction and retraction of plausible beliefs. To represent causation, the formalism incorporates the principle of Markov shielding which imposes a stratified set of independence constraints on rankings of interpretations. We show how this formalism resolves some classical problems associated with specificity, prediction and abduction, and how it offers a natural way of unifying belief revision, belief update, and reasoning about actions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #506. 	[Title] Rationality and its Roles in Reasoning [Abstract] The economic theory of rationality promises to equal mathematical logic in its importance for the mechanization of reasoning. We survey the growing literature on how the basic notions of probability, utility, and rational choice, coupled with practical limitations on information and resources, influence the design and analysis of reasoning and representation systems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1102364. 	[Title] Impediments to Universal Preference-Based Default Theories [Abstract] Research on nonmonotonic and default reasoning has identified several important criteria for preferring alternative default inferences. The theories of reasoning based on each of these criteria may uniformly be viewed as theories of rational inference, in which the reasoner selects maximally preferred states of belief. Though researchers have noted some cases of apparent conflict between the preferences supported by different theories, it has been hoped that these special theories of reasoning may be combined into a universal logic of nonmonotonic reasoning. We show that the different categories of preferences conflict more than has been realized, and adapt formal results from social choice theory to prove that every universal theory of default reasoning will violate at least one reasonable principle of rational reasoning. Our results can be interpreted as demonstrating that, within the preferential framework, we cannot expect much improvement on the rigid lexicographic priority mechanisms that have been proposed for conflict resolution. Please summarise all the given information.

Paper #504 has the title `Constructive Belief and Rational Representation  
` and the abstract `It is commonplace in artificial intelligence to divide an agent's explicit beliefs into two parts: the beliefs explicitly represented or manifest in memory, and the implicitly represented or constructive beliefs that are repeatedly reconstructed when needed rather than memorized. Many theories of knowledge view the relation between manifest and constructive beliefs as a logical relation, with the manifest beliefs representing the constructive beliefs through a logic of belief. This view, however, limits the ability of a theory to treat incomplete or inconsistent sets of beliefs in useful ways. We argue that a more illuminating view is that belief is the result of rational representation. In this theory, the agent obtains its constructive beliefs by using its manifest beliefs and preferences to rationally (in the sense of decision theory) choose the most useful conclusions indicated by the manifest beliefs.
`.
Question: Which category should Paper #504 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #504 has the title `Constructive Belief and Rational Representation  
` and the abstract `It is commonplace in artificial intelligence to divide an agent's explicit beliefs into two parts: the beliefs explicitly represented or manifest in memory, and the implicitly represented or constructive beliefs that are repeatedly reconstructed when needed rather than memorized. Many theories of knowledge view the relation between manifest and constructive beliefs as a logical relation, with the manifest beliefs representing the constructive beliefs through a logic of belief. This view, however, limits the ability of a theory to treat incomplete or inconsistent sets of beliefs in useful ways. We argue that a more illuminating view is that belief is the result of rational representation. In this theory, the agent obtains its constructive beliefs by using its manifest beliefs and preferences to rationally (in the sense of decision theory) choose the most useful conclusions indicated by the manifest beliefs.
`.
Question: Which category should Paper #504 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Probabilistic_Methods