Guide Embodying Intelligence in Structures and Integrated Systems

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Contents:
  1. An integrative view of brain-context interaction
  2. Cerebral boundaries: roots of neuropsychological assessment
  3. Embodied intelligence for leaders and coaches | Cultivating Leadership
  4. Embodied Cognition
  5. The new Nissan LEAF – embodying Nissan Intelligent Mobility

Though the Internal Models principle warns against over doing it, some internal modeling of the environment will be needed to allow for expectations important to perception, for instance , planning, problem solving, etc. This principle also points to the distinction between reactive and deliberative action selection see for example Sloman Deliberative actions are selected with the help of internal models, planners, schedulers, etc.

These models use internal representations in the strict sense of the word, that is, they are consulted for their content. Internal states that play a purely causal role without such consultations are not representations in this sense Franklin Chapter Reactive actions are exemplified by reflexes and routines Agre, in press.

They are arrived at without such consultation. Brustiloni's instinctive and habitual behaviors would be reactive while his problem solving and higher behaviors would be deliberative Deliberative mechanisms such as planners and problem solvers may well require their own memories and workspaces not shown in the figure. Figure 7.

The Coordination Principle warns us against unnecessary communication. Still, for a cognitive agent in a society of other such, communication may well be needed. It is sufficiently important that some people include it in their definition of an agent Wooldridge and Jennings VMattie communicates with humans by email.

Her understanding of incoming messages is part of perception. Her composition of outgoing messages are brought about by deliberative behaviors. Independent modules for understanding messages and for composing messages must be part of a general cognitive agent architecture.

Figure 8. The Knowledge Principle brings up two issues: building knowledge into the reactive behaviors, and learning. By definition, knowledge is built into reactive behaviors casually through their mechanisms, rather than declaratively by means of representations. This doesn't show up in the diagrams. The other issue brought up by the Knowledge Principle is learning, which is critical to many autonomous agents coping with complex, dynamic environments, and must be included in a general cognitive agent architecture.

Learning, itself, is quite complex. Thomas lists eight different types of learning as follows: 1 habituation-sensitization, 2 signal learning, 3 stimulus-response learning, 4 chaining, 5 concurrent discriminations, 6 class concepts: absolute and relative, 7 relational concepts I: conjunctive, disjunctive, conditional concepts, 8 relational concepts II: biconditional concepts. He uses this classification as a scale to measure the abilities of animals to learn. Perhaps the same or a similar scale could be used for autonomous agents. Thomas' classification categorizes learning according to the sophistication of the behavior to be learned.

One might also classify learning according to the method used. Maes lists four such methods: memory based reasoning, reinforcement learning, supervised learning, and learning by advice from other agents Drescher's concept learning and Kohonen's self-organization are other methods. The Curiosity Principle is directly concerned with reinforcement learning, while the Routines Principle speaks of compiling or chunking sequences of actions, again a form of learning. Figure 9. The limitations of working with an almost planer graph become apparent in Figure 9.

Learning mechanisms should also connect to drives and to memory, essentially to everything. And, there are other connections that need to be included, or to run in both directions. Well, we've run out of our sources of guidance, both action selection paradigm tenets and design principles.

An integrative view of brain-context interaction

Are we then finished? By no means. Our general architecture for a cognitive agent is still in its infancy. Much is missing. Our agent's motivations are restricted to drives and the goal trees that are grown from them. We haven't even mentioned the goal-generators that grow them. We also haven't discussed other motivational elements, such as moods, attitudes, and emotions which can influence action selection.

Cerebral boundaries: roots of neuropsychological assessment

For such a discussion, see the work of Sloman , , Sloman and Croucher Perception can have a quite complex architecture of its own Marr , Sloman , Kosslyn and Koeing , including workspaces and memories. Each sensory modality will require its own unique mechanisms, as will the need to fuse information from several modalities. Each of the deliberative mechanisms will have its own architecture, often including workspaces and memories. Each will have its own connections to other modules. For instance, some set of them may connect in parallel to perception and action selection Ferguson Similarly, each of the various learning mechanisms will have its own architecture, connecting in unique ways to other modules.

The relationship between sensing and acting, where acting facilitates sensing, isn't yet specified in the architecture. And, the internal architecture of the action selection module itself hasn't be discussed. Finally, there's a whole other layer of the architecture missing, what Minsky calls the B-brain Minsky , and Sloman calls the meta-management layer Sloman This layer watches what's going on in other parts of our cognitive agent's mind, keeps it from oscillating, improves it strategies, etc. And after all this, are we through?

There's Baars' notion of a global workspace that broadcasts information widely in the system, and allows for the possibility of consciousness There seems to be no end. As you can see, the architecture of cognitive agents is the subject, not for an article, but for a monograph, or a ten-volume set. Question Q2 about the necessary elements of embodied architectures is not an easy one if, as I do, you take cognitive agents to be the proper objects of study for embodied AI. So, how should embodied AI research proceed? Here's a "concrete proposal.

The contention here is that a holistic view is necessary.

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Intelligence cannot be understood piecemeal. That's not to say that projects picking off a piece of intelligence and studying its mechanisms aren't valuable. They often are. The claim is that they are not sufficient, even in the aggregate. Running the engineering loop and the science loop in parallel will enable the synergy between. This will mean making common cause with cognitive scientists and cognitive neuroscientists.

Strive to understand not only individual agent architectures, but the space of all such architectures. This means exploring, classifying, and theorizing at a higher level of abstraction. Each agent occupies a particular niche in its environment.

Embodied Cognition

Explore, in the same way, the space of such niches and the architectures that are suitable to them. Ackley, David, and Littman, Michael, Redwood City, Calif. Agre, Philip E. The Dynamic Structure of Everyday Life.


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Cambridge: Cambridge University Press. Albus, J. Baars, B. A Cognitive Theory of Consciousness. Beer, R.


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    Pittsburgh: Carnegie Mellon University. Drescher, G. AAAI Press. Kosslyn and Koeing Made-up Minds. MIT Press. Edelman, G. New York: Basic Books.