Steve Hanks and Drew McDermott. Nonmonotonic logic and temporal projection. Artificial Intelligence, 33(3):379--412, 1987.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... tail pipe there are lots of other disqualifying, albeit unlikely, obstacles, how can we ensure that after checking the tail pipe it does not become clogged during us walking to the front door and taking a seat, prior to trying to start the engine first illustrated with the Yale Shooting domain [ Hanks and McDermott, 1987 ] which occurs when neglecting causality in tackling the frame problem. Imagine the following scenario: We can put a potato into the tail pipe whenever no abnormal disqualification prevents us from doing so (e.g. the potato surprisingly turns out to be too heavy) likewise we can start the ....

....tail pipe is, after all, what one would normally expect. The reader might notice the similarities to the Yale Shooting problem: A gun that becomes magically unloaded while waiting deserves being called abnormal, whereas causality explains the death of the turkey if being shot at with a loaded gun [ Hanks and McDermott, 1987 ] The only existing alternative to global minimization of abnormalities as an approach to the qualification problem is based on chronological ignorance [ Shoham, 1987; Shoham, 1988 ] The basic idea there is to assume away by default abnormal, disqualifying circumstances, and simultaneously to ....

S. Hanks and D. McDermott. Nonmonotonic logic and temporal projection. Artificial Intelligence, 33(3):379--412, 1987.

....Calculus as regards reasoning about narratives, and combines it with the paradigm of Situation Calculus which supports reasoning about hypothetical sequences of actions. 2 Example: a Shooting Scenario Let s enlist an old standby as our running example: a variation on the Yale Shooting Scenario [2]. Suppose we know that, in general, shooting at a vase causes it to shatter, provided the gun is loaded. Likewise, shooting at a turkey with the gun loaded always kills it. Suppose further a specific narrative telling us that initially the vase is in one piece and the turkey is alive. The ....

S. Hanks and D. McDermott. Nonmonotonic logic and temporal projection. Artificial Intelligence, 3:379--412, 1987.

....ultimate conclusion. This is essentially a side stepping of the non triviality condition for activators. The non triviality condition should be strengthened. Fixing the flaws in Poole s rule is important because this kind of comparison is the comparison used in the Yale Shooting Problem arguments [6], as exhibited among the examples in the paper by Simari Loui. The rule also suffers in an example reminiscent of Royal Elephants [7] Consider compared with what should be an inferior theory: D Gamma Gamma B B Gamma Gamma E. Neither is more specific by Poole s rule. The example appears ....

Hanks, S. and D. McDermott. "Nonmonotonic logic and temporal projection," Artificial Intelligence 33, 1987.

....two forms A causes F if P 1 , P n and A causes F (2) Again, the second abbreviates the first when A always causes F , i.e. when n = 0. Many examples, considered as benchmarks in the area, have been represented in A. For instance, the domain of the famous Yale Shooting problem, cf. [17], has been described in [13] as follows. There are two (propositional) fluent names: Loaded and Alive , and three action names: Load, Shoot and Wait. initially initially Alive Load causes Loaded if Loaded (3) Thus, in this domain we have some knowledge about the e#ects of performing ....

Hanks, S., and D. McDermott. Nonmonotonic logic and temporal projection. Artificial Intelligence, 33(3) (1987) pp.:379--412.

....exceptions called the exception first principle [15] that is, when you want to apply a rule, first check if its exception is not plausible. It is a crucial feature to allow IDL discriminative skills, making it able to successfully avoid the anomalous extension problem as the Yale Shooting [7]. Here we are concerned in providing IDL with a sequent calculus formulation, one which goes uniformly from its deductive part to its nonmonotonic one. In our formulation, we had to use a kind of structural rule not really orthodox in the sequent calculus practise. The employment of this kind of ....

Hanks, S. & McDermott, D. `Nonmonotonic Logic and Temporal Projection'. Artificial Intelligence, 33:27-39, 1980.

....also consistent. A formal proof for this set of action descriptions being consistency preserving is analogous to the respective proofs for the Blocksworld domain in [16] Hierarchies of classes can be used in general to describe scenarios of actions and change. e.g. the Yale Shooting environment ([14]) consists of a gun which might be unloaded or loaded, a turkey which is alive or dead, and three actions, viz. loading the gun ( load ) shooting ( shoot ) and waiting ( wait ) The reader is invited to prove that the following set of action descriptions forms a complete set for the various ....

S. Hanks and D. McDermott. Nonmonotonic logic and temporal projection. Artificial Intelligence, 33(3):379--412, 1987.

....normal forms EPDL provides a formal language via action descriptions to describe the behavior and internal relationships of a dynamic system. These specify the effects and feasibility of actions, causal ramifications and other domain constraints. Example 1. Consider the Yale Shooting Problem [11]. Let Flu = alive, loaded, walking and ActP = Load, Shoot, W ait . This problem can be specified by the following action description: # # # # # # # # # [Load]loaded [Shoot]alive [Shoot]loaded [alive]walking #Load##, #W ait##, #Shoot## # # # # # # # # # ....

S. Hanks and D. McDermott, Nonmonotonic logic and temporal projection. Artificial Intelligence, 33(3):379-412, 1987.

....action effects by the extended dynamic logic. 3.1 Action description An action description of a dynamic system is a set of formulas which specifies effects of actions, causal relations, domain constraints and qualifications of action execution. Example 2 Consider the Yale Shooting Problem in [Hanks and McDermott 1987]. Let Flu alive, loaded, walking and Actp Load, Shoot, Wait . Then this problem can be described by the following action descrip tion: loaded [Load]loaded loaded [Shoot]alive loaded [Shoot]loaded [alive] walking (Load)T, Wait)T, Snoot) T The first three sentences ....

S. Hanks and D. McDermott, Nonmonotonic logic and temporal projection, Artificial Intelligence, 33(3):379-412, 1987.

....VARIATIONS OF THE YSP 50 9 Time situated Variations of the YSP In the previous section we described the application time situated reasoning mechanism to dead line planning. This mechanism was also applied in [ Nirkhe and Kraus, 1995 ] to several real time variations of the Yale Shooting Problem [ Hanks and McDermott, 1987 ] appropriate to active logics. In the classical YSP problem, there is a certain ambiguity about the role of the reasoner. There the reasoning is itself timeless, presumably it takes place after all the events in question. Our treatment is significant in that Dudley the reasoner can reason in ....

S. Hanks and D. McDermott. Nonmonotonic logic and temporal projection. Artificial Intelligence, 33:379--412, 1987.

....of enforcing a desired notion of specificity. Gelfond and Przymusinska [35] show how to embed inheritance reasoning into autoepistemic logic and how to incorporate required specificity notions to autoepistemic reasoning. A similar phenomenon emerges in reasoning about actions. Hanks and McDermott [43] come to the conclusion that standard nonmonotonic logics such as circumscription and default logic are inherently incapable of representing certain kind of default reasoning after examining a simple problem of reasoning about actions (the Yale shooting problem) They attempt to capture reasoning ....

S. Hanks and D. McDermott. Nonmonotonic logic and temporal projection. Artificial Intelligence, 33:379--412, 1987.

....inference engine. We claim that a crucial part of uncertain reasoning is not the process of forming a best conclusion from the facts known so far, but rather of knowing where to turn when those facts fall short. When confronted by a paradox such as the Nixon Diamond, or the Yale shooting problem [Hanks], commonsense dictates that more information is required. For example, the Nixon Diamond queries Is Nixon a pacifist from a knowledge base consisting of the four predicates Nixon is a Republican , Nixon is a Quaker , Quakers are (normally) pacifists , Republicans are not (normally) ....

S. Hanks and D. V. McDermott, Nonmonotonic logic and temporal projection, Artificial Intelligence., 33, (1987) 379-412.

....is if q is true and is (considered) possible, then Z is preferred . When = where = stands for syntactical equality) the default is called normal. In [MH1] and [Me] we have shown several examples of how to use this formalism for defaults including the non trivial Yale Shooting Problem ([HMcD]) 19 Tweety is a bird , together with the common sense default birds normally fly in the background, creates the expectation that Tweety will fly. Next, in contrastive statements our expectations are refuted immediately by the part following but . Whereas in (pure) default reasoning we ....

S. Hanks & D. McDermott, Nonmonotonic Logics and Temporal Projection, Artificial Intelligence 33 (1987), pp. 379-412.

....between a method which is applied to an object and an action which is executed in a certain situation. Rather we will use the notions object and situation as well as method and action interchangeably. As a second, more expressive example, we consider the famous Yale Shooting domain (cf. [24, 4]) The Yale Shooting environment consists of a gun which is unloaded or loaded, a turkey which is alive or dead, and three actions, viz. loading the gun ( load ) shooting ( shoot ) and waiting ( wait ) Figure 2 depicts the hierarchical structure of all consistent situations in this domain ....

S. Hanks and D. McDermott. Nonmonotonic logic and temporal projection. Artificial Intelligence Journal, 33(3):379--412, 1987.

....the warehouse is very busy, it may disappear after only a short while. The problem of finite assertion persistence has been recognized for some time in the literature, and different solutions have been suggested for different situations [Nilsson, 1980, McDermott, 1982, Dean, 1985, Shoham, 1986, Hanks and McDermott, 1987, Firby and McDermott, 1987] The simplest solution is to assign each assertion type in the database a specific life time. For example, assertions of type full might be assigned a life time of 1 hour. If a fuel drum is discovered to be full and the assertion 42 (full item 23 true) made, queries ....

....or that a specific item property like color or size has been measured. Notifications of item properties refer to items by their locally assigned sensor nanms. Primitive actions and sensor notifications for the robot truck sinrelator are given in Appendix B and discussed in some detail in Firby and Hanks [1987b] The hardware interface between the RAP system and the robot delivery truck sinrelator handles the execution of action requests and the interpretation of their results. The interface takes each action request generated by the RAP interpreter and passes it on to the sinrelated hardware for ....

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Steve Hanks and Drew McDermott. Nonmonotonic logic and temporal projection. Artificial Intelligence, 33, 1987.

....calculus. The situation calculus is a very appealing language which has been used to investigate many problems related to formal reasoning about change. For example, the language has been the formalism of choice for a great number of researchers interested in the so called frame problem (e.g. [40, 20, 21, 27, 54, 33, 9, 47]) So much so, that some researchers identify the frame problem as characteristic of theories based on the situation calculus, instead of as a problem inherent to the formalization of dynamic systems. Unfortunately, as has been pointed out elsewhere (e.g. 16, 23, 55] the original situation ....

....4.3 What Occurs 4.3.1 Motivation. Adding the occurs predicate to the language does not introduce any complications in the case in which all the events that occur are completely determined. However, problems do arise when this is not the case. For instance, consider the Yale Shooting Problem [21]; it is common to pose it as the problem of determining the truth value of the literal: holds(Alive; do(Shoot; do(W ait; do(Load; S 0 ) That is, we want to know whether a fluent holds in a completely determined situation. This problem can be formulated in a different fashion. In particular, ....

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Hanks, S., and McDermott, D. Nonmonotonic logic and temporal projection. Artificial Intelligence 33, 3 (1987), 379--412.

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Steve Hanks and Drew McDermott. Nonmonotonic logic and temporal projection. Artificial Intelligence, 33(3):379--412, 1987.

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Hanks S. and McDermott D., Nonmonotonic logic and temporal projection, Articial Intelligence, 33:379412, 1987.

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S. Hanks and D. McDermott. Nonmonotonic logic and temporal projection. Artificial Intelligence, 33:379--412, 1987.

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Steve Hanks and Drew McDermott, Nonmonotonic logic and temporal projection. , Artificial Intelligence, 1987, pp. 33(3):379--412.

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Steve Hanks and Drew McDermott. Nonmonotonic logic and temporal projection. Artificial Intelligence, 33(3):379-- 412, 1987.

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Steve Hanks and Drew McDermott. Nonmonotonic logic and temporal projection. Artificial Intelligence, 33:379--412, 1987.

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S. Hanks and D. McDermott, Nonmonotonic logic and temporal projection, Artificial Intelligence, 33(3): 379-412, 1987.

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S. Hanks, and D. McDermott. Nonmonotonic logic and temporal projection. AIJ, 33(3): 379-412, 1987.

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S. Hanks and D. McDermott. Nonmonotonic logic and temporal projection. Artif. Intell., 33(3):379--412, 1987.

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S. Hanks and D. McDermott. Nonmonotonic logic and temporal projection. Artificial Intelligence, 33(3):379--412, 1987.

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