Download A Box of Nothing by Peter Dickinson PDF

By Peter Dickinson

For one younger boy, a field choked with not anything is a price tag to adventure…

While skipping tuition, James sees his mom in the street. He geese inside of an deserted shop, the place an elderly shopkeeper asks what he desires to purchase. whilst James says “nothing,” the outdated guy sells it to him: a heavy cardboard field filled jam-packed with best not anything. James attempts to provide an explanation for this to his mom, yet she doesn’t think him and throws the field over the fence and into the sell off. He sneaks in to retrieve his new possession—and reveals himself trapped in one other world.

The sell off is an eerie position populated via hyperintelligent rats, great seagulls, and a truly shrewdpermanent pile of rubbish referred to as the Burra. as soon as it was once a thriving neighborhood, yet anything unusual has occurred, and the sell off has develop into caught in time. To come back domestic, James needs to support the Burra retailer the dump—using the entire not anything he can find.

This publication gains an illustrated own background of Peter Dickinson together with infrequent pictures from the author’s assortment.

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Extra info for A Box of Nothing

Example text

Da Silva Neves, ‘An experimental analysis of possibilistic default reasoning’, in KR2004, pp. 130–140. AAAI Press, (2004). [2] S. Benferhat, J. F. Bonnefon, and R. M. Da Silva Neves, ‘An overview of possibilistic handling of default reasoning: An experimental study’, Synthese, 146, 53–70, (2005). [3] S. Benferhat, D. Dubois, L. Garcia, and H. Prade, ‘On the transformation between possibilistic logic bases and possibilistic causal networks’, International Journal of Approximate Reasoning, 29, 135–173, (2002).

7 CONCLUDING REMARKS We have presented a simple qualitative model of the causal ascriptions an agent will make from its background default knowledge, when confronted with a series of events. In addition to supplementing this model with a do(•) operator, we intend to extend our present work in three main directions. First, we should be able to equip our framework with possibilistic qualitative counterparts to Bayesian networks [3], since System P augmented with Rational Monotony can be represented in possibilistic logic [4].

1) are met both for A, B, C and for do(A), B, C, where do(A) means that the occurrence of A is forced by an intervention [22]. As for now, we only give a brief example of how such an operator can be used in our approach. Example 6 (Yellow teeth). An agent learns that someone took up smoking, that this person’s teeth yellowed, and that this person developed lung cancer. , (C |∼ ¬Smoke, C |∼ ¬Yellow, C |∼ ¬Lung). The agent believes that it is normal for smokers to have yellow teeth (C ∧ Smoke |∼ Yellow) and to develop lung cancer (C ∧ Smoke |∼ Lung), and that it is not abnormal for someone who has yellow teeth to develop lung cancer (C ∧Yellow |∼ ¬Lung).

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