intention to give preference to the examples which I happen to be able to think of myself at this moment. It is strongly hoped that your community, because of its unique background and way of thinking, will be able to develop new approaches, to fill in this critical blank spot in our present knowledge, where there is hardly any existing literature to compete with.

## 2 Bliss And Brain-Like Intelligence

Many years ago, D.O. Hebb - one of the grandfathers of the neural network field - proposed that higher order intelligence in the brain results as a kind of emergent phenomenon. He proposed that we could replicate that kind of intelligence, simply by discovering the right "general neuron model," including the equations to adapt that general neuron. he proposed that we could develop an artificial intelligent system, simply by hooking together trillions of such model neurons, hooking them up to sensor input and motor output, and letting them learn everything from experience. 

From an engineering viewpoint, there are reasons to doubt that this could be done, using Hebb's ideas about learning, and one type of neuron only. However, for my Harvard PhD 
thesis proposal, back in 1972, I suggested that we might achieve Hebb's dream, by using three types of neuron - one to implement a "critic" network, one to learn a dynamic model of the external world, and one to execute actions. (See Werbos in Pribram 1994, and Werbos 1994.) 
Backpropagation was part of this design - as was the very first reinforcement learning design linked to dynamic programming. (See Werbos 1998a.)
As late as 1992 or 1994, this seemed like a reasonable goal. In any event, there are reasons to believe that neural network designs in this family can outperform traditional methods for intelligent control in a wide variety of applications (Werbos 1999) and in terms of rigorous stability results (Werbos 1998a). Based on this general vision, and based on the views of control theory, I proposed that we could achieve (mammal) 
brain-like intelligence by advancing and coupling basic research in three critical topics: (1) improved supervised learning (neural or nonneural), which provides the basic building blocks for more complex systems; (2) learning based system identification, which provides the necessary prediction network; (3) approximate dynamic programming 
(in relation to other control methods), which could provide the overall architecture of an intelligent system - including the critic and action components.

This kind of design has done better and better in practical engineering applications, but the gap between this approach and the capabilities of the brain has become more and more apparent. In a long paper cited in Werbos (1999), I 
summarize what the nature of the gap is. First, many artificial intelligence (AI) people have long argued that there is a difference between control and decision. For example, the problem of how to move your muscles at every moment is problem in control. But the problem of deciding where to go to college is a decision problem - it conditions your actions and life for years to come. Originally, we hoped that a good capability at making decisions would emerge from a simpler architecture, without a need to impose any kind of hierarchy or stratified system of controls and decisions. But recent work, both in biology and in analysis of learning methods, suggests that this approach is false. I have developed (and applied for a patent on) a generalized Bellman equation, considerably more general and flexible than some alternative approaches suggested by Sutton, which provides a starting point for the design of such a hierarchical learning-based decision-making system. In general, I would consider the area of "temporal chunking" (decision making as opposed to control in the narrow sense) to be one of the four new topics which requires more research - interdisciplinary research combining the best of what is known, at least, in AI and neural networks. I would also regard the idea of "multiple models" to be one possible testbed or formulation of this research task. Related to this task is the area of spatial chunking, which I 
will discuss further below. This essentially involves three sets of similar tasks: (1) the exploitation of symmetry or "object structure" to construct neural networks which do not have fixed numbers of inputs and outputs; (2) true spatial chunking - as in creating condensed roadmaps from complex aerial views; (3) world modeling - the management of decision problems when the world we see (even including some filtering and picturing of nearby objects out of sight) is actually a very small part of the larger world we want to cope with.

Now: where does the third new task, BLiSS, come into it? Very simply - the problem of local minima versus global minima starts to become overwhelming when we start to think in terms of decisions as opposed to control. For example, we can easily use traditional control methods to control our hand, to make it place a "Go" piece onto the 
"Go" board, to a desired location. But in deciding WHERE to move, we basically face 361 local minima. (each grid point on a 19 by 19 board.) We desperately need options. Biologically (Werbos in Pribram 1998), the commitment to a decision seems to take place in an area called the basal ganglia, which was once very mysterious but is now becoming better understood. The development of options seems to take place at a layer of the higher cerebral cortex (layer V or VI, I forget which), which is in fact known to have some stochastic behavior. It is also fascinating to consider that our image of reality may also be stochastic, 