The QEKG System : Generating Explanations from a Non-Discrete Knowledge Representation 1

The QI3KG system produces critical analyses of possible mo\ es fiir a uidc \ar~cty of backgammon positions, using a hicrarchicaliy su-ucturcd. non-discrete folm of knowledge rcprcscntation. ‘I‘hiq report compares discrete and continuous representations and reasoning systems, addressing issues of competence, robustness, and explainability. ‘l‘he QLWG system is described and demonstrated. Discrete systems vs Continuous systems Most work in knowlcdgc representation for artificial intelligence systems has used some variety of “discrctc” representation and control structure, from the condition-action rules of production systems [9], to a variety of frame-based systems [I 1. 41, to various sorts of semantic network [IO, 71. Thcsc systems have in common the property that at any given time there is an unequivocal distinction bctwccn what knowlcdgc is rekvarzt and what is not (with rclcvancc criteria such as “those productions whose condition portion is satisfied”, “those scripts that arc activated”, or “those nodes with marker 2 set.“) This all-ornone assumption incrcascs the cficicncy of thcss systems by reducing the cffcctivc size of the knowlcdgc base, and makes construction of the knowlcdgc base simpler by guaranteeing modularity. The price cxactcd for this simplicity can be high. however, in terms of system behavior. As discussed below. such systems tend towards anomalous behavior in certain circumstances, and are typically very sensitive to noise. Some more recent work with thcsc rcprcscntations has centered on relaxing the all-or-none assumption in various ways, such as allowing for partial matches in the condition-part of production rules [2] and the various spreading-activation thcorics in semantic nets [5]. Another reason that discrctc systems seem natural stems from the fact that all systems must cvcntually make basically all-or-none decisions about their actions. The traditional view seems to assume that the discrctcncss of the ultimate action implies that it will have discrctc justifications. with discrctc reasons for the justifications and so on. until the discrctc inputs arc rcachcd. A major altcrnativc scheme was advanced by workers intcrcstcd in game-playing systems, in the construction of knowlcdgc-intcnsivc evaluation functions for games such as backgammon, whcrc the clcmcnt of chance introduces a 1. ihlq march WI! , s~onaorcd by the lkfcnsc Advanced Kcscnrch Projects Agency (1 )()I)). AftPA Order Ko. 35%‘. monttolcd by 111~ Au IWcc Avionics laboratory Under Contim 1:33hlS-78-C-1551, hrnnching factor that makes suh<tantial exhaustive starch infeasible. In Lliis p*t,~Ji~nl, kl~in+IcJ& i:, I.CpI. CSci~lCd bq appropriate mathematical combination of‘ obscr\i;ttions on the world, and the control mechanism is the evaluation of the overall function on a sclcction of legal successor states, intcrpretcd as a “gathering of evidence” procedure. Discretization is held off until the last possible moment. when the evaluations of the successor states are compared and the one with the largest evaluation is chosen. In this scheme, the rclcvancc criterion for a knowledge item is fLj77y. with potentially all of the knowledge base implicated to some degree in each evaluation. For a large enough kno\vlcdgc base. this might suggest that implementation on a uniprocescor would be slow: however, the structure is well suited to a parallel implementation. It should be noted that even the choice of “discrete” versus “continuous” reprcscntations is not a discrete choice: in fact. there are varying dcgrccs of continuity possible, from the two-valued propositional rcprcscntaLion (e.g., “John is an adult” vs “John is not an adult”), to a finer grain representation (“John is in the 18 to 34 group”), to an essentially continuous rcprcscntation (“John is 26.d87 years old”). In many casts, the two-valued approach seems complctcly adequate (“John is a malt”), and it seems easy to ignore the odd boundary cases that occasionally crop up. Unfortunately, in many arcas of practical intcrcst there is a wide “gray arca” between the extremes (e.g., for age, duration, size, shape, color, beliefs, desires). and two-state (or n-state, for small2 n) systems tend to manifest undesirable behavior near the boundaries bctwccn the states. In society. for example, discre&ion leads to surprising behaviors such as prctcnding to bc “over 18”, or driving from Massachusetts to New Hampshire to buy liquor. Rcrliner has shown [3] that Samuel’s USC of non-linearity in an attempt to improve his checkers program probably foundcrcd on this problem. ‘I’hc important point is that too large a grain size for an observation can have a disastrous effect on system behavior. The extra information in a fine-graincd observation can always bc discarded higher in the knowlcdgc structure if it is not nccdcd, such as when the digital watch tells us it is “4:56:34” and we think “five o’clock. ” In other circumstances. such as timing an egg, WC would maintain a finer grain. It is clear that the nccdcd grain size varies depending on the task at 2 “s&r’ hcrc ilnplying fial large mmbcrs of usefully distinguishable obwvations SC bciug “buckctcd” to@hcr. 213 From: AAAI-82 Proceedings. Copyright ©1982, AAAI (www.aaai.org). All rights reserved.