Abstract: Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency — as though these inferences are a reflex response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the results about the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuron-like elements represent a large body of systematic knowledge and perform a range of inferences with such speed? We describe a computational model that is a step toward addressing the cognitive science challenge and resolving the artificial intelligence paradox. We show how a connectionist network can encode millions of facts and rules involving n-ary predicates and variables, and perform a class of inferences in a few hundred msec. Efficient reasoning requires the rapid representation and propagation of dynamic bindings. Our model achieves this by i) representing dynamic bindings as the synchronous firing of appropriate nodes, ii) rules as interconnection patterns

We are capable of drawing a variety of inferences effortlessly, spontaneously, and with remarkable efficiency — as though these inferences are a reflex response of our cognitive apparatus. This remarkable human ability poses a challenge for cognitive science and computational neuroscience: How can a network of slow neuron-like elements represent a large body of systematic knowledge and perform a wide range of inferences with such speed? The connectionist model Shruti attempts to address this challenge by demonstrating how a neurally plausible network can encode a large body of semantic and episodic facts, systematic rules, and knowledge about entities and types, and yet perform a wide range of explanatory and predictive inferences within a few hundred milliseconds. Relational structures (frames, schemas) are represented in Shruti by clusters of cells, and inference in Shruti corresponds to a transient propagation of rhythmic activity over such cell-clusters wherein dynamic bindings are represented by the synchronous firing of appropriate cells. Shruti encodes mappings across relational structures using high-efficacy links that enable the propagation of rhythmic activity, and it encodes items in long-term memory as coincidence and conincidence-error detector circuits that become active in response to the occurrence (or non-occurrence) of appropriate coincidences in the on going flux of rhythmic activity.

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For connectionist networks to be adequate for higher level cognitive activities such as natural language interpretation, they have to generalize in a way that is appropriate given the regularities of the domain. Fodor and Pylyshyn (1988) identified an important pattern of regularities in such domains, which they called systematicity. Several attempts have been made to show that connectionist networks can generalize in accordance with these regularities, but not to the satisfaction of the critics. To address this challenge, this paper starts by establishing the implications of systematicity for connectionist solutions to the variable binding problem. Based on the work of Hadley (1994a), we argue that the network must generalize information it learns in one variable binding to other variable bindings. We then show that temporal synchrony variable binding (Shastri and Ajjanagadde, 1993) inherently generalizes in this way. Therebywe show that temporal synchronyvariable binding is a connect...

In order to understand language, a hearer must draw inferences to establish referential and causal coherence. Hence our abilityto understand language suggests that we are capable of performing a wide range of inferences rapidly and spontaneously. This poses a challenge for cognitive science: How can a system of slow neuron-likeelements encode a large body of knowledge and perform inferences with suchspeed? shruti attempts to answer this question by demonstrating howaneurally plausible network can encode a large body of semantic and episodic facts, and systematic rule-likeknowledge, and yet perform a range of inferences within a few hundred milliseconds. This paper describes a novel representation of types and instances in shruti that supports the encoding of rules and facts involving types and quanti#ers, enables shruti to distinguish between hypothesized and asserted entities, and facilitates the dynamic instantiation and uni#cation of entities during inference.

Efficient knowledge representation and reasoning is an important component of intelligent activity, and is a crucial aspect in the design of large-scale intelligent systems. This dissertation explores the design, analysis, and implementation of massively parallel knowledge representation and reasoning systems which can encode very large knowledge bases and respond to a class of queries in real-time, with reasoning episodes expected to span a fraction of a second. The dissertation attempts to design efficient, large-scale knowledge base systems by: (i) exploiting massive parallelism; and (ii) constraining representational and inferential capabilities to achieve tractability, while still retaining sufficient expressive power to capture a broad class of reasoning in intelligent systems. To this end, shruti, a connectionist reasoning system which models reflexive--- i.e., effortless and spontaneous---reasoning serves as the knowledge representation and reasoning framework. Shruti-based mas...

The relationship between symbolism and connectionism has been one of the major issues in recent Artificial Intelligence research. An increasing number of researchers from each side have tried to adopt desirable characteristics of the other. A major open question in this field is the extent to which a connectionist architecture can accommodate basic concepts of symbolic inference, such as a dynamic variable binding mechanism and a rule and fact encoding mechanism involving n-ary predicates. One of the current leaders in this area is the connectionist rule-based system proposed by Shastri and Ajjanagadde. We demonstrate that the mechanism for variable binding which they advocate is fundamentally limited and show how a reinterpretation of the primitive components and corresponding modifications of their system can extend the range of inference which can be supported. Our extension hinges on the basic structural modification of the network components and further modifications of the rule a...

Shastri and Ajjanagadde have described a neurally plausible system for knowledge representation and reasoning that can represent systematic knowledge involving n-ary predicates and variables, and perform a broad class of reasoning with extreme efficiency. The system maintains and propagates variable bindings using temporally synchronous---i.e., in-phase --- firing of appropriate nodes. This paper extends the reasoning system to incorporate multiple instantiation of predicates, so that any predicate can be instantiated up to k times, k being a system parameter. The ability to accommodate multiple instantiations of a predicate allows the system to handle a much broader class of rules, including bounded transitivity and recursion. The time and space requirements increase only by a constant factor, and the extended system can still answer queries in time proportional to the length of the shortest derivation of the query. Introduction In (Shastri & Ajjanagadde, 1990a, 199...

An implementation of a distributed connectionist network of spiking neuron-like elements is presented. Spiking nodes fire at a precise moment and transmit their activation, with particular strengths and delays, to nodes connected to them. The receiving nodes accumulate potential, but also slowly their potential through decay. When the potential of the node reaches a particular threshold, it emits a spike. Thereafter, the potential is reset to a resting value. As with real neurons, there is a short refractory period during which this node will be completely insensitive to incoming signals, after which its sensitivity will slowly increase. Precise timing properties are used to represent symbols in a distributed manner and to solve the problems of variable binding and multiple instantiation. Several predictions about human short-term memory, predicate processing, complex reasoning, and multiple instantiation arise from this model. This network shows how symbolic processing can be achieved using neurologically and psychologically plausible mechanisms that also have the advantage of generalization and noise tolerance found in connectionist networks.

Recently, shruti has been proposed as a connectionist model of rapid reasoning. It demonstrates how a network of simple neuron-like elements can encode a large number of specific facts as well as systematic knowledge (rules) involving n-ary relations, quantification and concept hierarchies, and perform a class of reasoning with extreme efficiency. The model, however, does not deal with negated facts and rules involving negated antecedents and consequents. We describe an extension of shruti that can encode positive as well as negated knowledge and use such knowledge during reflexive reasoning. The extended model explains how an agent can hold inconsistent knowledge in its long-term memory without being "aware" that its beliefs are inconsistent, but detect a contradiction whenever inconsistent beliefs that are within a certain inferential distance of each other become co-active during an episode of reasoning. Thus the model is not logically omniscient, but detects contradictions whenever...