This paper presents a novel learning model Clarion , which is a hybrid model based on the two-level approach proposed in Sun (1995). The model integrates neural, reinforcement, and symbolic learning methods to perform on-line, bottom-up learning (i.e., learning that goes from neural to symbolic representations). The model utilizes both procedural and declarative knowledge (in neural and symbolic representations respectively), tapping into the synergy of the two types of processes. It was applied to deal with sequential decision tasks. Experiments and (...) analyses in various ways are reported that shed light on the advantages of the model. (shrink)

This paper addresses weighting and partitioning in complex reinforcement learning tasks, with the aim of facilitating learning. The paper presents some ideas regarding weighting of multiple agents and extends them into partitioning an input/state space into multiple regions with di erential weighting in these regions, to exploit di erential characteristics of regions and di erential characteristics of agents to reduce the learning complexity of agents (and their function approximators) and thus to facilitate the learning overall. It analyzes, in reinforcement learning (...) tasks, di erent ways of partitioning a task and using agents selectively based on partitioning. Based on the analysis, some heuristic methods are described and experimentally tested. We nd that some o -line heuristic methods performed the best, signi cantly better than single-agent models. (shrink)

We present a skill learning model CLARION. Different from existing models of high-level skill learning that use a topdown approach (that is, turning declarative knowledge into procedural knowledge), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. CLAR- ION is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line learning. We compare the model with human data in a minefield navigation task. A match between the model and (...) human data is found in several respects. (shrink)

This paper introduces a hybrid model that unifies connectionist, symbolic, and reinforcement learning into an integrated architecture for bottom-up skill learning in reactive sequential decision tasks. The model is designed for an agent to learn continuously from on-going experience in the world, without the use of preconceived concepts and knowledge. Both procedural skills and high-level knowledge are acquired through an agent’s experience interacting with the world. Computational experiments with the model in two domains are reported.

To deal with reactive sequential decision tasks we present a learning model which is a hybrid connectionist model consisting of both localist and distributed representations based on the two level approach proposed in..

This paper introduces a hybrid model that combines connectionist, symbolic, and reinforcement learning for tackling reactive sequential decision tasks by a situated agent. Both procedural skills and high-level symbolic representations are acquired through an agent's experience interacting with the world, in a bottom-up direction. It deals with on-line learning, that is, learning continuously from on-going experience in the world, without the use of preconstructed data sets or preconceived concepts. The model is a connectionist one based on a two-level approach proposed (...) earlier. Acknowledgements: This work is supported in part by O ce of Naval Research grant N00014-95-1-0440. (shrink)

In developing autonomous agents, one usually emphasizes only (situated) procedural knowledge, ignoring more explicit declarative knowledge. On the other hand, in developing symbolic reasoning models, one usually emphasizes only declarative knowledge, ignoring procedural knowledge. In contrast, we have developed a learning model Clarion, which is a hybrid connectionist model consisting of both localist and distributed representations, based on the two-level approach proposed in Sun (1995). learns and utilizes both procedural and declarative knowledge, tapping into the synergy of..