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GIL - an experiment in goal-directed inductive learning
Abstract
GIL, a goal-directed inductive learning program, is presented. GIL learns without environment-dependent heuristics and without the "special" help of a teacher by modeling an abstraction of instrumental conditioning. In addition, GIL uses a secondary goal value learning mechanism as a substitute for state-space searching. GIL's learning of four problems is presented: (1) the game of tic-tac-toe, (2) a maze problem in which the ability to learn a novel maze was tested as a function of previous exposure to similar mazes, (3) a dual goal problem, and (4) a pattern recognition problem, in which "friend" and "foe" patterns were to be distinguished.
... A number of years ago I explained to a coworker how my dissertation program (Portegys 1986), a model of instrumental/operant conditioning, could learn various tasks through reinforcement. He then asked me how "smart" it was. ...
A bstract
Honey bees are social insects that forage for flower nectar cooperatively. When an individual forager discovers a flower patch rich in nectar, it returns to the hive and performs a “dance” in the vicinity of other bees that consists of movements communicating the direction and distance to the nectar source. The bees that receive this information then fly to the location of the nectar to retrieve it, thus cooperatively exploiting the environment. This project simulates this behavior in a cellular automaton using the Morphognosis model. The model features hierarchical spatial and temporal contexts that output motor responses from sensory inputs. Given a set of bee foraging and dancing exemplars, and exposing only the external input-output of these behaviors to the Morphognosis learning algorithm, a hive of artificial bees can be generated that forage as their biological counterparts do.
... Mona has a simple interface with the environment, shown in Figure 2, similar to that utilized by Portegys (1986) and Nolfi and Parisi (1996). All knowledge of the state of the environment is absorbed through "senses"; there are no special modalities or channels by which instructions or meta-information are given. ...
Goal-seeking behavior in a connectionist modelis demonstrated using the examples of foragingby a simulated ant and cooperativenest-building by a pair of simulated birds. Themodel, a control neural network, translatesneeds into responses. The purpose of this workis to produce lifelike behavior with agoal-seeking artificial neural network. Theforaging ant example illustrates theintermediation of neurons to guide the ant to agoal in a semi-predictable environment. In thenest-building example, both birds, executinggender-specific networks, exhibit socialnesting and feeding behavior directed towardmultiple goals.
... Mona uses the same "hardware" as a predecessor, GIL (a Goal-directed Inductive Learner) [6], interacting with its environment as shown in Figure 1. All knowledge of the state of the environment is absorbed through "senses"; there are no special modalities or channels by which instructions or meta-information are given. ...
This paper presents Mona, a connectionist model of motivation
implemented by a network of neuron-like components whose interactions
drive behavior toward goals which reduce homeostatic needs. The network
incorporates a short term memory capability allowing it to model a
state-space with relatively few neurons
To answer the questions of how information about the physical world is sensed, in what form is information remembered, and how does information retained in memory influence recognition and behavior, a theory is developed for a hypothetical nervous system called a perceptron. The theory serves as a bridge between biophysics and psychology. It is possible to predict learning curves from neurological variables and vice versa. The quantitative statistical approach is fruitful in the understanding of the organization of cognitive systems. 18 references.
Briefly describes frame systems as a formalism for representing knowledge and then concentrates on the issue of what the content of knowledge should be in specific domains. Argues that vision should be viewed symbolically with an emphasis on forming expectations and then using details to fill in slots in those expectations. Discusses the enormous problem of the volume of background common sense knowledge required to understand even very simple natural language texts and suggests that networks of frames are a reasonable approach to represent such knowledge. Discusses the concept of expectation further including ways to adapt to and understand expectation failures. Argues that numerical approaches to knowledge representation are inherently limited.