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Goal-directed cognition is often discussed in terms of specialized memory structures like the "goal stack." The goal-activation model presented here analyzes goal-directed cognition in terms of the general memory constructs of activation and associative priming. The model embodies three predictive constraints: (1) the interference level, which arises from residual memory for old goals; (1) the strengthening constraint, which makes predictions about time to encode a new goal; and (3) the priming constraint, which makes predictions about the role of cues in retrieving pending goals. These constraints are formulated algebraically and tested through simulation of latency and error data from the Tower of Hanoi, a means-ends puzzle that depends heavily on suspension and resumption of goals. Implications of the model for understanding intention superiority, postcompletion error, and effects of task interruption are discussed.

Means-ends analysis is a seemingly well understood search technique, which can be described, using planning terminology, as: keep adding actions that are feasible and achieve pieces of the goal. Unfortunately, it is often the case that no action is both feasible and relevant in this sense. The traditional answer is to make subgoals out of the preconditions of relevant but infeasible actions. These subgoals become part of the search state. An alternative, surprisingly good, idea is to recompute the entire subgoal hierarchy after every action. This hierarchy is represented by a greedy regression-match graph. The actions near the leaves of this graph are feasible and relevant to a sub. . . subgoals of the original goal. Furthermore, each subgoal is assigned an estimate of the number of actions required to achieve it. This number can be shown in practice to be a useful heuristic estimator for domains that are otherwise intractable. Keywords: planning, search, means-ends analysis Reinven...

Classical planning is the problem of finding a sequence of actions to achieve a goal given an exact characterization of a domain. An algorithm to solve this problem is presented, which searches a space of plan prefixes, trying to extend one of them to a complete sequence of actions. It is guided by a heuristic estimator based on regression-match graphs, which attempt to characterize the entire subgoal structure of the remaining part of the problem. These graphs simplify the structure by neglecting goal interactions and by assuming that variables in goal conjunctions should be bound in such a way as to make as many conjuncts as possible true without further work. In some domains, these approximations work very well, and experiments show that many classical-planning problems can solved with very little search. 1 Definition of the Problem The classical planning problem is to generate a sequence of actions that make a given proposition true, in a domain in which there is perfect informati...

This report is a description of a new prototype of UC so designed

In this paper, we examine the motivations for research on cognitive architectures and review some candidates that have been explored in the literature. After this, we consider the capabilities that a cognitive architecture should support, some properties that it should exhibit related to representation, organization, performance, and learning, and some criteria for evaluating such architectures at the systems level. In closing, we discuss some open issues that should drive future research in this important area. Key words: cognitive architectures, intelligent systems, cognitive processes 1

Human-computer interaction (HCI) is the study of how people design, implement, and use interactive computer systems and how computers affect individuals, organizations, and society. This encompasses not only ease of use but also new interaction techniques for supporting user tasks, providing better access to information, and creating more powerful forms of communication. It involves input and output devices and the interaction techniques that use them; how information is presented and requested; how the computer’s actions are controlled and monitored; all forms of help, documentation, and training; the tools used to design, build, test, and evaluate user interfaces; and the processes that developers follow when creating interfaces. This report describes the historical and intellectual foundations of HCI and then summarizes selected strategic directions in human-computer interaction research. Previous important reports on HCI directions include the results of the

Abstract. Many real-world applications involve the management of large amounts of time-dependent information. Temporal database systems maintain this information in order to support various sorts of inference (e.g., answering questions involving propositions that are true over some intervals and false over others). For any given proposition, there are typically many different occasions on which that proposition becomes true and persists for some length of time. In this paper, these occasions are referred to as time tokens. Many routine database operations must search through the database for time tokens satisfying certain temporal constraints. To expedite these operations, this paper describes a set of techniques for organizing temporal information by exploiting the local and global structure inherent in a wide class of temporal reasoning problems. The global structure of time is exemplified in conventions for partitioning time according to the calendar and the clock. This global structure is used to partition the set of time tokens to facilitate retrieval. The local structure of time;is exemplified in the causal relationships between events and the dependencies between planned activities. This local structure is used as part of a strategy for reducing the computation required during constraint propagation. The organizational techniques described in this paper are quite general, and have been used to support a variety of powerful inference mechanisms. Integrating these techniques into an existing temporal database system has increased, by an order of magnitude or more in most applications, the number of time tokens that can be efficiently handled.

This thesis investigates problem-solving in domains featuring incomplete information and multiple agents with opposing goals. In particular, we describe Finesse --- a system that forms plans for the problem of declarer play in the game of Bridge. We begin by examining the problem of search. We formalise a best defence model of incomplete information games in which equilibrium point strategies can be identified, and identify two specific problems that can affect algorithms in such domains. In Bridge, we show that the best defence model corresponds to the typical model analysed in expert texts, and examine search algorithms which overcome the problems we have identified. Next, we look at how planning algorithms can be made to cope with the difficulties of such domains. This calls for the development of new techniques for representing uncertainty and actions with disjunctive effects, for coping with an opposition, and for reasoning about compound actions. We tackle these problems with a...

This paper describes LICAI, a model that simulates peforming tasks by exploration where the tasks are given to the user in the form of written exercises that contain no information about the correct action sequences. LICAI's comprehension processes and the action planning processes are based on Kintsch.'s .constmc. tion-in,tegrafio n theo for text comprehension. The model comprehends me mstmcuons ano generates goats which are then stored in memory. The action planning process is eontroled by goals retrieved from memory cued by displays generated by the application.