Automatic market arbitrage

Our discussion of the inference rules in table 3 above has implicitly made the assumption that selecting the appropriate good from several others is a simple operation. For example, the Generalize Operator rule implies that an agent who wants to buy a seller's choice Science query, $\begin{picture} (10,10) \put(5,3){\circle{10}} \put(2,0){S}\end{picture}$[Science], can buy a buyer's choice Science query, $\begin{picture} (10,10) \put(5,3){\circle{10}} \put(1,-1){B}\end{picture}$[Science], and pick an arbitrary Science topic. There is clearly a potential trade between the buyer and seller, which can occur just by making an arbitrary selection between topics. If the choice is process is well-defined, it could be automated either within an individual agent or by having a specialized $\begin{picture} (10,10) \put(5,3){\circle{10}} \put(1,-1){B}\end{picture}$-to-$\begin{picture} (10,10) \put(5,3){\circle{10}} \put(2,0){S}\end{picture}$arbitrage agent created on demand to link the two markets.

  

Figure 3: Arbitrage Rules on structured Query Planning Services

Figure 3 shows how these rules, using appropriate control of the rule execution process, can generate a large number of different products, based on a few simple transformations. This suggests that one-time requests for products requiring simple transformations may be more efficiently handled through automatically generated arbitrage agents or by encoding simple transformations within agents, compared to starting up an entirely new market.