Combining belief and utility in a structured connectionist agent architecture

Combining Belief and Utility in a Structured Connectionist Agent Architecture Carter Wendelken and Lokendra Shastri International Computer Science Institute 1947 Center Street, Suite 600 Berkeley, CA 94704 carterw,shastri @icsi.berkeley.edu type hierarchy Abstract The SHRUTI model demonstrates how a system of sim- ple, neuron-like elements can encode a large body of re- lational causal knowledge and provide the basis for rapid inference. Here we show how a representation of util- ity can be integrated with the existing representation of belief, such that the resulting architecture can be used to reason about values and goals and thereby contribute to decision-making and planning. +e fact type buy(Person,-,-) +v +e +v entity To understand how the brain creates the mind, one could work mainly from the top down, characterizing mental processes, or from the bottom up, trying to understand the capabilities of neurons and simple circuits. In devel- oping the SHRUTI model we have pursued both these ap- proaches simultaneously in order to understand how net- works of neurons can perform complex cognitive tasks. In past work, we have demonstrated how such networks can make predictive and explanatory inferences with re- spect to a large body of causal knowledge. In this paper, we show how the SHRUTI architecture can be extended to represent and reason not only about beliefs but also about utilities, values and goals. The resulting model uses a single causal structure to seek explanations, make predictions, and identify expected utilities of world states and actions. The SHRUTI architecture First we present the basic elements of the SHRUTI archi- tecture. The model is described in considerably more detail in [Shastri, 1999, Shastri and Ajjanagadde, 1993, Shastri and Wendelken, 2000]. SHRUTI is a neurally plausible (connectionist) model that demonstrates how a network of neuron-like elements could encode a large body of structured knowledge and perform a variety of inferences within a few hundred milliseconds. SHRUTI suggests that the encoding of relational information (frames, predicates, etc.) is mediated by neural circuits composed of focal clusters and that the dynamic rep- resentation and communication of relational instances involves the transient propagation of rhythmic activity across these clusters. A role-entity binding is represented in this rhythmic activity by the synchronous firing of ap- propriate cells. ?e ?v Person ?e Man John focal cluster Introduction ?v enabler buy(buyer,thing,seller) collector role node to +e:Money rule mediator to ?e:Object rule pay(payer,payee,payment) Figure 1: Diagram showing core elements of the SHRUTI model, including relational focal clusters, a fact, a rule, and a simple type hierarchy. A focal cluster is a collection of nodes with varying functionality all subserving a common representation. A relational focal cluster consists of a positive (+) and a negative (-) collector node, an enabler (?) node, and role nodes. The activity of the positive (negative) collector node reflects the amount of evidence collected in sup- port of belief (disbelief) in the given relation. Activity of the enabler (?) node reflects the strength with which information about the relation is being sought. A link from collector to enabler ensures that the system auto- matically seeks explanation for what it believes. Role bindings are represented by synchronous firing of rela- tional role nodes with nodes in a connectionist type hi- erarchy. A relational cluster with active role bindings represents a relational instance. Rules are encoded with links that enable the propagation of rhythmic activity from one relational focal cluster to the next. Specif- ically, a rule is formed by linking the antecedent col- lector to the consequent collector, the consequent en- abler to the antecedent enabler, and matching role nodes in both directions, through an intervening focal cluster