If you want to lose, most of the time, then Ninja Gaiden is the game for you. I have to say that I am quite disappointed with this game, especially considering all the good reviews that it received. Generally, everything that people liked about the game was an optimistic assessment at best. The graphics are so-so, the gameplay is bad, and the difficulty is unreal.

Perhaps the greatest flaw in the game is the camera. Sure, it is nice to see the main character from time to time, but I would really like to know where the enemies are. Additionally, you don’t have a camera rotation option; all you can do is use the poor “center” option. I could on about this forever; I had lots of time to endure it while attempting to defeat the second boss seventy-six times.

Many people claim that the graphics in this game are just “super.” Well, I’m here to break it to you: they are not. Everything in the game feels dull and uninspired. They didn’t even trying to use lighting for atmosphere, there is just one global light setting throughout the entire game. If they want and area to be darker, the make the walls a dark color. I mean serious, that is pretty cheap. Compared to the average graphics of the current generation, you might say this has an edge on them, but it is nothing special.

I am honestly appalled by the gameplay mechanics in this game. Apparently, there are supposed to be a large assortment of moves, and combos that you can use in the game, but they are generally either too slow, or ineffective against regular enemies, so you are stuck doing the “quick” combos. The more I think about it, the more frustrated I get, so I’ll move on.

I cannot recall ever having played a game that is at difficult as this. I mean, it literally took me seventy-six attempts to defeat the second boss. Granted I was low and power, and without any healing items…that just sucked. Additionally, you can die in any given battle, even the lowliest thug can easily dispatch of you unless you are near perfect in the confrontation. At my current save point, as soon as I load the game I am attacked by four black ninjas. So, I start trapped in a cubby and get my ass kicked, so far I have only survived getting out of there once. I have to say that the difficulty rating really takes all the fun out of the game. You never really get a chance to enjoy any moment, because you are constantly trying not to slip up which will cause your instant death. I’ll probably talk more about this in the future.

I guess I could make a brief comment about there story: there really isn’t one. It follows the typical action game style of brief cut scenes between chapters that give little tidbits of story, but are generally weak and uninspired. Which, I suppose is a good thing, since you never get a chance to think about it when you are playing, you are too busy getting your ass handed to you.

Something good, what was something good…oh yeah, the level design in the game is good. It is setup so you can go through a diverse area that makes it feel like you have no idea where you are, but then you round the bend, unlock a door, and you are back in the main area. It does a pretty decent job of not making you backtrack too much.

Other than the above comment, there is not a whole lot of redeeming value to the game. Really, the only reason that I am still playing it is because I am so pissed off about constantly dieing. Granted I am only a third of the way through the game, I would probably rate this a two on a scale of one to five. Perhaps my opinion will change, but that is what I think now.

The Particle Swarm

The chapter starts out by talking about some concepts that were covered in the first half of the book, and why they make the particle swarm an obvious solution. The area they cover with the largest amount of depth is the adaptive culture model. When discussing this area they talk about the three primary concepts of the sociocognitive underpinnings, which are evaluate, compare, and imitate. Then, they go into discussion about each of these aspects.

The next area of focus is the model of binary decision. They begin by talking about what a binary decision model is. Then, they discuss the two types of neighborhoods that are used in binary modes: lbest (local), and gbest (global). They go into depth about each of these methods, and then begin talking about how you evaluate binary strings. There is then discussion of the reasoned action model; this is addressed when trying to figure out how to improve cognitive fitness. From there, they begin developing mathematical models for determining the probability of and individual deciding yes or no. After discussing the aspects of the mathematical backings, they give an example algorithm for optimizing goodness.

The next area of discussion is the testing of the binary algorithm using the De Jong test suite. In this section they look at several different functions, their dimensionality, and the performance of the binary swarm on the different functions. There are some bit string examples in order to help understand some of the work done by the binary particle swarm on the functions.

As in any field, there is some controversy regarding the evaluation of an algorithm. The discussion says that a majority of this strife is created by David Wolperet and William Macready, whom state that the performance of all algorithms is the same when averaged over all possible problems (or costs). The “no free lunch” theorem shows how all algorithms can be considered equal when averaged across the problem space. The section then describes the NFL problem, and then proceeds to defend itself against the argument, stating that you can determine which algorithm is better towards finding a goal, even if it is not necessarily good at finding answers to questions that no one would ever ask.

The next area of discussion is multimodality. In this context they are referring to problems that have more than one solution (or global optimum). After discussion what a multimodal problem is, they go into discussion as to why these problems are difficult for genetic algorithms to handle. They talk about three forms of genetic algorithms (mutation and crossover, crossover only, and mutation only), and how they perform on various problems against number evaluations and their peak fitness. Then, just for kicks they try to show how particle swarm can handle these problems better, and illustrate the PS performance against the GA’s performance.

“Minds as parallel constraint satisfaction networks” is the next focus of interest. They start out by talking about Hopfield, and his contributions to the field. Next, they begin discussion of binary and continuous Hopfield networks. They begin talking about having setup a binary particle swarm to optimize the network structure proposed by Hutchins. They then go into an example and explain the way the PS optimized Hutchins’ problem.

The next section deals with particles swarms that handle continuous numbers. The section begins by explain how this is the “real” particle swarm, and sets up for the explanation. The first area they discuss is the particle swarm in real number space. Essentially, particles in a real number space are connected to topographical neighbors, and neighbors tend to cluster in the same regions of search space. After that discussion they go into some mathematical background. There is then discussion of pseudocode for particle swarm optimization in continuous numbers. The next area they address, are the issues associated with the implementation of this version of the particle swarm. Having setup the foundation they go into an example of the particle swarm optimization of neural net weights. The section continues with a discussion of real world applications of the particle swarm. In this case they are referring to PSO for “training” neural nets rather than using back propagation.

The next section focuses on the hybrid particle swarm. They begin by briefly explain what a hybrid system would consist of, and then try to explain why you might want to implement such a system. They use a system that would diagnosis various abdominal diseases, and explain how some sections are more easily computed using binary PS, whereas more complex symptoms are computed using real PS. The presentation is merely hypothetical, and the hybrid system is still considered and on going research area.

The following section takes a look at science as a collaborative search. The section talks about null hypothesis testing, and confirmation bias. There is also discussion about the difference between truth and certainty. Then they talk about the establishment of paradigms in the scientific community. There is then discussion about mistakes that have been done when dealing with human social search, and problem space, which they believe is due to tendency of individuals to move towards self and social confirmation of hypotheses. The premise of which is logically invalid, results in excellent information processing capabilities.

The final section takes a quick look at emergent culture, and immergent intelligence. They begin by talking about trends that develop throughout multiple iterations within the population of the PS. Then, they talk about polarization, and optimal solutions either consuming lesser solutions, or compromising and thus moving away from an optimum. Then they begin to talk about he emergence of cultures within the programs, and how they are not hard coded, and care difficult to predict. Next, they discussed the process of the immergence of cognitive adaptation among the individuals. The section is ended with their perspective on the importance of the emulation of cognitive positions allowing individuals to adapt.

Thinking is Social

The chapter begins by talking about the story of the blind men and the elephant. The point of the story here is to show the societies are able to benefit from the sharing of individuals partial knowledge, which results in a large body of knowledge that alloww the group to develop strategies no individual could formulate. They then begin talking about the three levels of adaptation, the point of these three levels are the development of optima processes in a group. The adaptive culture model is the next topic of interest. First they briefly review their earlier discussion of Axelrod, and his contribution to evolutionary computing. Then they begin talking about speculations that have been made regarding the development optimums through the use of cognitive optimization. Axelrod’s recent simulations are touched on before they address the next topic.

Axelrod’s culture model is the next topic of interest in the chapter. It begins by talking about how similarities between individuals can be used to spread culture. In the ACM model individuals adopt non-matching features from their neighbors stochastically. It then talks about how simulations are conducted by repeated iteration until regions of the matrix contain matching patterns. The following sections detail various experiments that were conducted using ACM.

The first experiment deals with Axelrods theory that similarity is a precondition for social interaction and subsequent exchange of cultural features. There is then discussion on about the “birds of a feather flock” idea where self-similar individuals group, another idea presented where by people are more interested in group with people that share the same ideas. In this experiment the effect of similarity as a casual influence was removed. The result of the experiment was unanimity. Thus, it appears that the effect of casual similarity in ACM results in polarization.

In the second experiment they substituted a simple arbitrary function for the similarity test previously used. The rule was “if (the neighbor’s sum is larger than the targeted individual’s sum) then interact.” In this experiment the population converged on the global optimum every time, though the number “9999” was never included in the initial population.

The task of the third experiment was to find a set of five numbers that represented the features of an individual, within which the sum of the first three numbers equaled the sum of the last two. They go into discussion about why this is interesting, and relevant. Then they explained the details of the experiment. The result of the experiment was that all the individuals solved the problem, and parts of the solution were distributed through definite regions of the matrix. They believe the point of this experiment is to show the spread of features throughout a culture.

The fourth experiment deals with “hard” problems, also known as NP problems. In this particular experiment, they looked at the traveling salesman problem. They talk about the details of setting up the TSP to work within the simulation, and then make some observations about the results they received across multiple tests. It seems that at best half of the population would find an optima path, but throughout the simulations they found five different paths that all yielded the shortest distance.

Parallel constraint satisfaction was the focus of the fifth experiment. It began by discussion how features of ACM can be used to represent constraint satisfaction networks. They then go into discussion about parallel constraint satisfaction networks. Discussion of the advances and disadvantages of various aspects of these networks followed. An example, and setup for the experiment was the next topic of discussion. They go into detailing how these networks were encoded for the sample, and then talk about their observations from the experiment.

The sixth experiment focuses on symbol processing. There is discussion about traditional AI and navigation through symbolic nodes. Then, there is a more detailed discussion of how a network of nodes is transformed into a hierarchical tree. From the hierarchical tree they example the properties that are used in this experiment. There is some discussion at the end about the relevance of the experiment.

The chapter ends with a discussion about the ACM, and important questions related to it. Then they begin to talk about the relative insignificance of the individual in the system. And, finish by trying to make a global comparison to human thinking, and cognition.

“I was surprised as well at the connection of Pretty Woman to Cinderella. It got me thinking of other stories that follow the same theme but it could be argued that every mellowdrama with the poor heroin, the handsome hero, and the evil villian could be included. I suppose in some way “Its a Wonderful Life” with Jimmy Stewart and Donna Reed could have been fashioned from Cinderella. Jimmy never realizing his dreams and being stuck in a situation and the angel showing him his value.”

That is pretty much that same thing I was thinking. The story is reminiscent “The Certain Thing,” where a poor girl acquires a Millionaire. I do think is interesting that you mentioned, “It’s a Wonderful Life” though. I think that the transformation is a bit different in the tale. As it seems that the change culminates from the dark recesses of his soul. In a way, you might consider it an ante-Cinderella. Since, in it the protagonists suffers more deeply than any other character, and thus is more happy about his life only because of the possible darkness he witnessed in the twilight. So, you might say he is a Cinderella that happily returned to the hearth, as the char girl. But, those are just my two cents.

Discuss you reaction to the different Cinderella stories you have heard. Which one’s did you like? What surprised you? Did you enjoy learning about the different versions of Cinderella? What do you think the variety of tales says about culture?

In general the presentation of various versions Cinderella was interesting. I think the stories that I enjoyed hearing about the most were the ones that had the greatest amount of departure from the traditional tale of Cinderella. For example, the Ring, which Aaron talked about, was particularly different and interesting. Hearing about the different version gave a great deal of perspective about collective ideals across cultures. It also helped illustrate the shaping of those foundational beliefs within the collective perspective of each respective culture.

The various versions of Cinderella do a fairly good job of showing how culturally common tales can be shaped by the prevailing views of the culture they are told in. This was particularly notable in the version I presented since it had some unusual elements that were carryovers from the original Gaelic version, but it also had strong Norse influence. However, you can also see the influence of more modern perspectives, as the beginning shifted towards the traditional version, and there was also the issue of Christian sermon. However, there are also major overtones from the last known version, Yei Shin (something like that) out of China, where foot binding was prevalent, yielding Cinderella with small feet. There are many more examples like this, but those are two particularly strong examples.

Also, for those of you actually interested in reading my thoughts, when I asked the question about how one of the groups determined how to choose the scope of the cultures I was not trying to be a jerk. My perspective on the issue was that even though Ireland, and the others do contain subcultures, they are not so different as those within America. The way I see it, is that in America we are a people united by a common government, and not a whole lot else. So, basically what my point was that by saying “American, Modern” they were taking a higher level of abstraction that with the other stories. But, that was just me being too picky. ;-p