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 #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 #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 #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 #1112650 has the title `On Decision-Theoretic Foundations for Defaults  
` and the 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.
`.
Question: Which category should Paper #1112650 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1112650 has the title `On Decision-Theoretic Foundations for Defaults  
` and the 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.
`.
Question: Which category should Paper #1112650 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