c 1994, IEEE. In The Tenth IEEE Conference on Arti cial Intelligence for Applications (CAIA-94). Copyright
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Subsumption and Recognition of Heterogeneous Constraint Networks Robert Weida Department of Computer Science Columbia University New York, NY 10027
[email protected]
Abstract Terminological knowledge representation (tkr) systems such as kl-one are widely used in AI to construct concept taxonomies based on subsumption inferences. However, current tkr systems are unable to represent temporal patterns or recognize instances of such patterns from ongoing observations. Motivated by applications such as service personnel dispatching and plan recognition for interactive user interfaces, we extend tkr by introducing terminological QME networks. In QME networks, nodes are tkr concepts and arcs are qualitative constraints between temporal intervals associated with nodes, metric constraints between endpoints of temporal intervals, and equality constraints among roles of di erent concepts. We use QME networks to represent patterns, and de ne QME network subsumption, which enables us to organize a pattern library into a taxonomy. We also develop a terminological approach to predictive pattern recognition based on subsumption and a related notion of compatibility. We assign a modality of necessary, optional, or impossible to every pattern as events and constraints are observed. We also show how to augment a pattern library for complete recognition. Our work, implemented in the t-rex system, enables more sophisticated applications of tkr technology.
AI Topic: Terminological knowledge representation. Domain area: Automated teller machine servicing. Language/Tool:LISP/CLOS, K-Rep or Classic, MATS. Status: Fully implemented prototype. Impact: Enabling technology for automatic or interactive response to temporal patterns of events.
1 Introduction
Terminological knowledge representation (tkr) systems are used to represent and reason with conceptual knowledge in many application areas [21, 2, 22, 6, 9, 17]. However, the applicability of contemporary tkr systems is limited by restrictions on both their representation language and their inferences [8]. Our work extends the scope and utility of tkr by representing complex constraint networks in a terminological framework, and by extending the range of terminological inferences that can be computed. Our approach integrates work in terminological systems, Robert Weida is also with the IBM Thomas J. Watson Research Center, Yorktown Heights, NY.
Diane Litman AI Principles Research Department AT&T Bell Laboratories Murray Hill, NJ 07974
[email protected]
temporal reasoning, and plan recognition. We illustrate our approach using examples from the domain of ATM (automated teller machine) service person dispatching. Current tkr systems focus on structured descriptions of classes of objects, or concepts, and the organization of concepts into de nitional taxonomies based on terminological inferences. In a de nitional taxonomy, each concept describes a set of possible instances which are a superset of those described by its descendant concepts. tkr systems compute subsumption (subset) relationships between concepts based on their de nitions. Classi cation via subsumption ensures that the proper location of any concept within the taxonomy is uniquely determined from its de nition. Classi cation thus endows a taxonomy with formal meaning, and provides many additional bene ts [5, 14, 20]. One limitation of current tkr systems is an inability to represent and reason with complex compositions of concepts. tkr systems have thus not been widely used in plan-based applications, where plans and actions are typically represented as temporal compositions of concepts. Our work extends tkr to terminological constraint networks, where each node is described by a concept in an existing tkr system, and constraints represent relationships between concepts. In [18], we extended tkr by constructing taxonomies of networks with qualitative temporal constraints, which we used to represent plans. Here we scale up our approach to handle the representational needs of other applications, as well as more sophisticated plan representations. We extend our representation to terminological QME (qualitative, metric and equality) networks: nodes are concepts in an existing tkr system, and arcs are qualitative temporal constraints between the temporal intervals associated with concepts, metric temporal constraints between the endpoints of temporal intervals, and equality constraints between the roles of concepts. We develop a notion of QME network subsumption, which extends the ideas of subsumption and classi cation found in TKR systems to automatically organize QME networks into a de nitional taxonomy. The advantages obtained from representing knowledge in standard terminological systems are thus achieved here as well. Another limitation of current tkr systems involves the type of recognition they support. In particular, tkr systems compute whether an individual object instantiates (is a member of) a concept. In contrast, a major thrust of plan-based reasoning is plan recognition, where the system computes not only whether a set of observations (i.e. individuals and constraints) instantiates a plan, but also
c 1994, IEEE. In The Tenth IEEE Conference on Arti cial Intelligence for Applications (CAIA-94). Copyright whether the observations are compatible with (can potentially instantiate) a plan. This predictive type of recognition has been used to support applications such as cooperative user interfaces [10]. In [18], we developed a new, terminological approach to plan recognition that dynamically partitioned the taxonomy into the modalities necessary, optional and impossible, according to the observations. Here we broaden our view to cover predictive, terminological pattern recognition using a library of networks composed of events related by QME constraints. We thus extend the ideas of subsumption and instantiation found in TKR systems to support more sophisticated types of recognition. We also show how to automatically augment the network taxonomy, to streamline the run-time recognition process. A fully implemented prototype system called t-rex (from Terminological RECognition System ! T-RECS ! T-REX) serves as a testbed for our ideas. A system diagram appears in Figure 1. t-rex represents and reasons about events and their constituents using a tkr system, either k-rep [15] or classic [4]. Metric and qualitative temporal constraints are represented and propagated using the mats temporal reasoning system [13]. When a network is de ned, t-rex checks syntactic correctness, completes the de nition by deriving implicit temporal and equality information, and classi es the network in the taxonomy by means of subsumption tests against previously de ned networks. The network representation is de ned in Section 2, the completion process in Section 3, and the network subsumption process in Section 4. When observations are presented, t-rex recognizes the modality of each network in the library using subsumption and compatibility inferences. This process is described in Section 5. Pattern Definitions
P A T T E R N R E A S O N E R
Observations
Completion
K-REP (CLASSIC)
Subsumption
Events & Entities Equality Constraints
Classification MATS Recognition
Qual. & Metric Constraints
T-REX Pattern Library & Observation Network Recognized Patterns
Figure 1: The T-REX System We are exploring the use of t-rex in ATM service dispatching, where a service dispatcher gets a sequence of error codes from an ATM and must determine the type of service person to dispatch to the ATM site. Throughout this paper we focus on the following scenario, created by the NCR Human Interface Technology Center based on their interviews of banking personnel: scenario 1: There have been 3 jams in an ATM bill transport mechanism in a 24 hour period. (The error code received is M05 - dispenser reject bin full). No 2nd line
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service person has been sent, though a 1st line person has been sent twice to clear the jam. When the third jam occurs, the system should create a trouble ticket that recommends dispatching a 2nd line service person.
2 QME network representation QME constraint network concepts represent classes, and are de ned by specifying a collection of tkr concepts, qualitative constraints between temporal intervals associated with each concept, metric constraints between endpoints of the temporal intervals, and equality constraints among roles associated with concepts. Network concepts denote a set of possible network instances that satisfy the constraints. In our domain, we use network concepts to describe patterns of types of error messages, and network instances to represent time-stamped collections of errors. (We discuss the use of QME constraint networks for plan representation in [19].) As in [18], event type constraints are speci ed by concepts in a standard tkr taxonomy. In our examples, tkr concept names are pre xed by c-, while tkr instances concatenate a concept name with a unique number and strip o the leading c-. The following de nition states that the tkr concept c-dispenser-reject-bin-full is a subclass of (is subsumed by) c-dispenser-error, and has two restrictions on llers of the role named machine: (define-concept c-dispenser-reject-bin-full (and c-dispenser-error (all machine c-high-capacity-atm) (exactly 1 machine)))
In particular, the value of a role may be restricted by another tkr concept (here, c-high-capacity-atm), and its cardinality restricted by a metric constraint whose lower bound is always at least zero. We also associate temporal intervals with tkr concepts. Qualitative constraints between temporal intervals i and j are represented as i(r1 _ ::: _ rn )j , where the ri are taken from [1]: before, meets, during, overlaps, starts, nishes, equals and inverses of the rst six relationships. Here we extend our network representation to QME networks by additionally representing metric (quantitative) and equality constraints. Metric constraints are conjunctions of two di erence inequalities on the endpoints of temporal intervals, as in [13]: (iF ? jG R n) ^ (jG ? iF Q m), abbreviated as ?m Q iF ? jG R n, where 1. ileft and iright are the starting and ending endpoints of interval i, 2. F; G 2 fleft;rightg, 3. R; Q 2 f;
