Research into developing human-level AI can be divided into the following areas:
Intelligent Agents
A recurring theme in many of the military and academic papers cited above are Intelligent Agents (IAs) that implement ‘smart behaviors’ or attempt to mimic personality traits of commanders or cultures. Laird’s work with ‘bots can also be put into the category of Intelligent Agents as they are both relatively small, selfcontained applets.
IAs are also becoming more common in commercial Role Playing Games (RPGs) to implement Non Player Characters (NPCs). There are now commercial third-party (middleware) packages for creating IAs (see DirectIA above).
IAs really do not introduce any new ideas in AI coding. An IA may be created using scripting, fuzzy logic, finite state machines, fuzzy state machines or even, theoretically, genetic algorithms. Their strengths include the ability to perform certain specific tasks (such as a QuakeBot’s ability to hide, ambush and fight) and their “set it and forget it” autonomy.
While they can greatly contribute to the authenticity of a simulation the path to human-level intelligence probably does not lead through Intelligent Agents.
Finite State Machines
Finite State Machines (FSMs) were described in Turing’s Computing Machinery And Intelligence (though he called them Discrete State Machines) over fifty years ago. To Turing an FSM was a physical device8.
Since then the term FSM, while it still can refer to hardware, is more likely to mean a series of AI routines that are linked together with specific entry and exit points. The following image is a detail from a software phone number FSM,
Obviously, FSMs are basic building blocks of AI and will continue to be so far into the future. However, it is their very nature of chopping real life actions into discrete compartments that makes me suspicious of FSMs playing more than an incidental or supporting role in the quest for Human-Level AI. Indeed, as Turing wrote, “Strictly speaking there are no such machines. Everything really moves continuously.”
Fuzzy Logic
While the Department of Defense has, not surprisingly, funded the largest computer wargames ever created (WARSIM, JWARS, TACOPS) they are all curiously devoid of strategic or even tactical AI. Indeed, when I sent email to a friend who works on WARSIM asking him about the AI used in the wargame he replied, “We don't really use AI, since the enemy is played by Humans, but we do have some pretty smart behaviors as well as a Fuzzy Logic system that attempts to portray some of the soft factors of war.”
Fuzzy Logic (which is described in numerous citations above) shows some promise for the creation of Human-Level AI. Humans rarely think in absolute terms. The driver’s education book states definitively that you are to signal a turn precisely100 ft. before the intersection but people never get out and measure the exact distance.
The same is true with military or strategic axioms. 
It is “good” to get behind an enemy. It is good to cut an enemy’s lines of supply and communication. However, there are no precise mathematical definitions for the terms.
In 1985 I unknowingly used fuzzy logic in my commercial wargame, UMS: The Universal Military Simulator (screen shots at right).
The program “understood” concepts such as “line” and “flanks”. This was accomplished by first drawing an imaginary box around all the units in an army, then determining the army’s facing by comparison with the enemy army. It was then easy to determine left and right flanks as well as the ‘center’ of the army’s lines (a double envelopment is simply a simultaneous left and right flank attack).
It is reasonable to assume that fuzzy logic will play a role in the development of Human-Level AI.
Neural Nets
Surprisingly it is the commercial gaming industry that is showing the most interest in Neural Nets. I did not find any references to Neural Nets currently being employed for military applications. André LaMothe, a very respected writer about computer game programming, has produced an in-depth piece on the subject (here neural.doc).
Neural Nets have gone in and out of style since they were first proposed over fifty years ago and are bound to become popular again. Neural Nets are very reminiscent of the “meat machine” view of the human brain popularized in the ‘60s. Indeed, part of the allure of Neural Nets is the almost homeopathic belief that mimicry of the wiring of the human brain will, eventually, produce a sentient, human-level intelligence. I remain skeptical.
Fuzzy State Machines
“A fuzzy state machine (FuSM) brings together fuzzy logic and finite state machine (FSMs). Instead of determining that a state has or has not been met, a FuSM assigns different degrees of membership to each state (Russel & Norvig, 2002). Therefore, instead of the states on/off or black/white, a FuSM can be in the states ‘slightly on’ or ‘almost off’. Furthermore, a FuSM can be in both the ‘on’ and ‘off’ states simultaneously to various degrees.” - Sweetser.
Fuzzy State Machines will almost certainly be a part of any attempt to create Human-Level AI.
A Life
A Life (Artificial Life) is the current “hot topic” in computer game AI. The umbrella term has been used to describe any program that emulates or simulates living creatures even though the techniques employed can be quite different.
The Sims, frequently cited as an example of A Life, actually use a system of Intelligent Agents (see Some notes on programming objects in The Sims™; Forbus; Wright).
Other A Life programs employ genetic algorithms. At this time the term ‘A Life’ is too amorphous to have any value in this discussion.
Scripting
Scripting refers to any system in which a previously created file of behaviors (often a text file) is loaded at runtime to control the actions of NPCs or units.
AI scripts, in a way, are not unlike “book openings” in chess. They are a series of “canned” responses. For The War College (1996) (screen shot at right) I created three scripts for each army that contained detailed instructions for the coordinated movements of each unit. At runtime one script picked at random was loaded. The computer AI continued executing the script until any of a number of triggers (hostile units approaching within so many meters, hostile units attacking, etc.) were encountered at which time a series of heuristic tactical combat routines took over.
Scripts can be a very powerful tool but they presuppose some knowledge of the simulation. This was possible because The War College contained simulations of the battles of Antietam, Pharsalus, Tannenburg and Austerlitz. Being familiar with the real events and knowing the terrain I could create reasonably intelligent scripts for the armies to follow.
Generic scripts could also be created that are not tied to a specific terrain or simulation such as football pass route scripts or guard patrol scripts. In the right circumstances, scripting can be a very powerful AI tool.
Genetic Algorithms
Genetic Algorithms (GAs) are an interesting concept; especially since they seem to promise to deliver quite a bit of AI for very little human coding. While a GA can, theoretically, deliver an ever more intelligent AI the downside is that GAs are notorious for consuming CPU memory and clock cycles. Because of this the commercial gaming world has not yet explored these methods.
Belief-based Bayesian Network.
I encountered only one example of this method (A Bayesian Network Representation of a Submarine Commander. Yu) and the results were less than impressive. Bayesian Networks
(see http://www.cs.ualberta.ca/~greiner/bn.html)
certainly seem to be very useful for representing uncertainty and making deductions, but they do not seem directly applicable to the creation of human-level AI.
Hierarchal AI.
Pottinger described a two-tier hierarchal AI as one of the methods that he used in his Ages of Mythology. 
The highest level AI, which Pottinger termed “AI Goals” are “super-high-level constructs such as ‘Attack Player 4’ or ‘Build a forward base in Player 3’s direction.’” Beneath this the lower level AI, dubbed “Plans” would then fetch the appropriate script and execute the behavior.
In my UMS II: Nations at War (1992) I designed a four-tiered hierarchal AI that corresponded to a nation’s Order of Battle Table (screen shot at right). For example, the highest level (here represented as Montgomery the 21st Army Group Commander) would pass down the order to attack the German player. The next level down, Bradley, would then assign objectives such as attack Berlin. The next level down, the VII Corps in this case, would then assign a series of way points utilizing the road net to Berlin (passing through Paris for example) and give orders to the divisions under its control. The lowest level units would then execute pathfinding algorithms to get on to the road net and advance toward its first waypoint (Paris).
A multi-tiered hierarchal AI system is crucial for emulating the behavior of complex military formations. Each tier of the hierarchy corresponds to an equivalent level of the Order of Battle Table.
Indeed, any Human-Level AI that is operating in a strategic simulation, be it a military campaign or a football game, will need to implement a hierarchal AI. I am certain of this.
Final Thoughts
In my research I did not encounter any program that had made substantial progress towards Human-Level AI as described in the chapter “The Problem” at the top of this paper. That was not surprising. What was surprising was that I could not find any research group even attempting to create AI of that caliber.
Research in developing Human-Level AI can be divided into two distinct areas: the commercial game industry and military sponsored research (which includes military funded academia). The two groups approach the problem differently because they have different motivations and interests.
Commercial computer game developers are primarily interested in creating a challenging opponent that can play the game that they designed. A great deal of the commercial gaming AI is ‘purpose built’ and frequently hardwired for a specific game. Furthermore, there is a long history of cheating in commercial computer game AI design. Even when it is not overtly cheating many of the AI routines that are now being created have no real value outside the original application. For example, one recent technique is for the level designer or artist to pre-plot waypoints to be used in lieu of heuristic pathfinding. Indeed, this is even a built in feature of AI. implant (see above).
At the same time, the wargames created for the military (WARSIM, JWARS, TACOPS) do not have any strategic AI whatsoever (recall the comment from my friend who works on WARSIM, “We don't really use AI, since the enemy is played by Humans…”). While creating Human-Level strategic AI is a daunting task, the failure to do so can be extraordinarily dangerous (recall V Corps commander Lt. Gen. William S. Wallace’s recent comments from Iraq, “The enemy we're fighting is a bit different from the one we wargamed against."). The enemy they had wargamed against was controlled by U. S. officers following U. S. military doctrine. What they needed was to wargame against an enemy controlled by a Human-Level AI that could think outside the box so that problems were encountered during simulations and not on the battlefield.
John Laird of the University of Michigan admits that there is not much of a technology transfer from academia to the commercial gaming industry, either (“When game developers look at AI research, they find little work on the problems that interest them, such as nontrivial pathfinding, simple resource management and strategic decision-making…” - Bridging the Gap Between Developers & Researchers).
Yet it is inevitable that Human-Level strategic AI will be created and it will probably be created by the end of this decade.
Most of the pieces to the puzzle are probably all ready out on the table. They just need to be assembled in a new way.
- David Ezra Sidran
July 27, 2003
Davenport, Iowa
8 “The digital computers considered in the last section may be classified amongst the 'discrete state machines' these are the machines which move by sudden jumps or clicks from one quite definite state to another. These states are sufficiently different for the possibility of confusion between them to be ignored. Strictly speaking there are no such machines. Everything really moves continuously.” Computing Machinery And Intelligence