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This is an example of a subtle type of communication over a long distance For example, a 1 might mean attack and a 0 might mean retreat Both parties could at least coordinate their effort What is odd about this scenario is that the person back home has equal probability of attacking or retreating The choice is random A substantial elaboration using entangled qubits involves additional qubits and also classical channels of communications as suggested in 1984 by Bennett and Brassard [Charles H Bennett (1942 ) and Gilles Brassard (1955 )] This gets into a subject called teleportation, in which it is theoretically possible for a 1-qubit superposition to materialize at a receiver with the aid of entangled qubits In practice, unfortunately, qubits entangled or otherwise cannot exist for long because of decoherence, or interference from the environment Entangled qubits are not easy to maintain, much less separate.

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Concerning the AND function no transformation can be found to convert p the state [1, 1, 1, 1, 1, 1, 1, 1]u= 8 into a basis vector This state vector has neither symmetry nor antisymmetry So by de nition, it is entangled..

(19.12)

TABLE 1.1 Sigma Table Sigma 6.0 5.0 4.0 3.0 2.5 2.0 1.5 1.4 1.3 1.2 1.1 1.0 0.5 0.0 Defects per Million 3.4 233.0 6,210.0 66,807.0 158,655.0 308,538.0 500,000.0 539,828.0 579,260.0 617,911.0 655,422.0 691,462.0 841,345.0 933,193.0 Yield 99.9997% 99.977 99.379 93.32 84.1 69.1 50.0 46.0 42.1 38.2 34.5 30.9 15.9 6.7

f(l) = -f(-I)

P r o p , if and only if p E n ( ( r u ) ) , ,

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Pollack (1993),Bell (1995)and Dongha (1995)A critique of Cohen and Levesque's theory of intention is presented in Singh (1992) Related works, focusing less on single-agent attitudes, and more on social aspects, are Levesque et 01 (1990),Jennings (1993a), Wooldridge (1994) and Wooldridge and Jennings (1994) Although I have not discussed formalisms for reasoning about action here, we suggested above that an agent logic would need to incorporate some mechanism for representing agent's actions Our reason for avoiding the topic is simply that the field is so big, it deserves a whole review in its own right Good starting points for A1 treatments of action are Allen (1984) and Allen et al (1990, 1991) Other treatments of action in agent logics are based on formalisms borrowed from mainstream computer science, notably dynamic logic (originally developed to reason about computer programs) (Harel, 1984; Harel et al, 2000).

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is de ned as exposure to 60 C for 1 min As the temperature is increased, the shorter exposure time equates to 1 PU The more organisms, the more extensive is the heat treatment, so the onus is on the operator to minimise the populations by good hygienic practice Cooling The ability of organisms to grow is curtailed as the temperature is lowered (refrigeration, freezing) Drying As organisms usually require signi cant amounts of water (discussed earlier), drying affords preservation Thus, for example, starting materials for fermentation (such as grains and fruits) may be subjected to some degree of drying if they are to be stored successfully prior to use The other way in which water activity can be lowered is by adding solutes such as salt or sugar.

Here s the formula modi ed to use the current date:

The logic of seeing to it that has been discussed in the formal philosophy literature, but has yet to impact on multiagent systems (Belnap and Perloff, 1988; Perloff, 1991; Belnap, 1991; Segerberg, 1989) See Wooldridge (1997) for a discussion on the possibility of using logic to engineer agent-based systems Since this article was published, several other authors have proposed the use of agents in software engineering (see, for example, Jennings, 1999) With respect to the possibility of directly executing agent specifications, a number of problems suggest themselves The first is that of finding a concrete computational interpretation for the agent specification language in question To see what we mean by t h s , consider models for the agent specification language in Concurrent MetateM These are very simple: essentially just linear discrete sequences of states.

Temporal logic is (among other things) simply a language for expressing constraints that must hold between successive states Execution in Concurrent MetateM is thus a process of generating constraints as past-time antecedents are satisfied, and then trying to build a next state that satisfies these constraints Constraints are expressed in temporal logic, which implies that they may only be in certain, regular forms Because of t h s , it is possible to devise an algorithm that is guaranteed to build a next state if it is possible to do so Such an algorithm is described in Barringer et al (1989)The agent specification language upon which Concurrent MetateM is based thus has a concrete computational model, and a comparatively simple execution algorithm.

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