Proactive automotive apps?

January 5, 2013

In a previous post, “Synergistic Social Agent Network Cloud” I argued for more proactive apps. I was just reading something that is related to that topic: “Ford Hybrid’s EV+ Feature Learns and Automatically Adjusts Powertrain to Deliver More Electric-Only Driving” Also see “Proactive Agents.”


The clunkiness of user interfaces

September 18, 2012

So much research and patents on UI design and interaction, yet we still have the most banal GUIs. For example, to share a web page on my Android phone I have to do about twenty operations. We don’t really notice how much we have to do since we are used to it and it becomes ‘natural’ like riding a bike.

In a new blog post I tackled this issue. I think those twenty steps can be easily reduced to two.

Links

Keywords

  • User Experience Design (UXD)
  • Interaction Design (IxD)

Can the predictive web also enable control?

June 23, 2012

In a previous post, “Synergistic Social Agent Network Cloud“, I discussed how a web of ‘agents’ could optimize ‘apps’ to be more responsive, proactive, and multipliers of our intents. I was just reading “Should we fear mind-reading future tech?” by Andrew Keen, and was thinking of the possible negative aspects. Still reading the article, so it may cover this. (finished reading it, was not mentioned).

Privacy is the usual concern about this high-tech stuff. This is very important. But, can “faster than realtime” computing, prediction, massive complex-event-processing and correlation also enable the powers that be to also control? We already know that advertising in all it’s forms can control, else why, for example, is the American presidential election a feeding fest of political money contributions?

Could that same advertising and fake news reporting via social media and apps that employ predictive quasi-AI morph into controlling media, an Orwellian manifestation of new-speak? In a scenario that would make a great sci-fi novel, Big Interests like political parties, business groups, and political organizations use social media, not only to advertise, but to gently guide one toward having programmed epiphanies.

Can it even be more “physical” and intrusive? For example, by prediction, these groups can arrange that one will meet a certain someone at the right time. Your a bleeding heart influential liberal? No problem, the future Fox News will arrange that you meet this gorgeous strong willed conservative that will change your mind.

Silly example, but you get the point, when you know, you can make nano-adjustments: unnoticeable, personalized, massive lobbying. Ads are old school, here come the psych-bots.


From Tags To Categories

February 5, 2012

Tags are usually non-hierarchical fine grained descriptors of a resource. They are the opposite of categories which are usually part of a semantic hierarchy. Categories are really old-school, killed by the effectiveness of Search and the expanding mash-up universe.

However, while tags provide rapid access to resources and easier sharing of them, tags do not enrich knowledge. We can see this if we consider knowledge as one of the tiers toward wisdom: data, information, knowledge, wisdom. Tags are data on information, metadata. Categories are information on knowledge.

Since data is used to create information, the data on information, tags, can be harnessed to increase the information on knowledge by the automated creation of categories. We simply create the directed cyclic graphs of tags around resources and detect clusters. The naming of categories come from the resources themselves.

This is how the internet will wake up. It will create an ontology and it will act. Hopefully, humans are part of a necessary category.

Some possible related reading

  1. Ontology on Wikipedia
  2. New Common Tag Format
  3. Semantic Web Use Cases and Case Studies. Case Study: Semantic tags
  4. Ontology is Overrated: Categories, Links, and Tags

Pedestrian route production patent

January 3, 2012

Interesting patent issued to Microsoft, “Pedestrian route production“.

This could be seen as application of the general ‘agent’ concepts I wrote about in one of my prior blog posts on Software Agents, “Synergistic Social Agent Network Cloud“.

Further Reading


Synergistic Social Agent Network Cloud

November 28, 2010

Summary

Multi-agent systems embedded in the social cloud offer more value then ‘Apps’.

Key words: agents, multi-agent, dynamic networks, social networks, Recommender system, cloud, cloud agent

Background

Mobile apps have not been very gratifying. Testing an app last year gave some clarity to what I felt to be a problem with the current App ecosystem. And, this is not just a mobile issue, but also for traditional computing platforms. I have been thinking of this subject for years. This is just, finally, a very simple and pragmatic example.

Scenario

Last year I downloaded an app that locates the cheapest gas based on my current location. Whether cheap gas should be used in one’s car is not the point here. The app could have been one for finding the best licensed massage therapist or bookstore. The point, is this using mobile computing to its full potential?

What if the cheap gas station is located in an area where crime is very high? Should I risk a carjacking just to save 3 cents? What if I’m about to run out of gas now, is the cheapest gas too far away? We can get even more complicated of course. What if I have to be at an appointment, shouldn’t the cheap criteria be augmented with route info; the cheapest gas is the one easiest to get to on my way to or from my appointment.

In short, the current app is one-dimensional. Real life is multidimensional and the human brain easily makes decisions within this mostly analog fuzzy chaos. If an app cannot make decisions or recommendations in that same world, it collapses the dimensions, it is a dumbing down.

Solution

How can the app be made more dimensional? AGENTS. The app should really be an Agent that cooperates with other agents to fulfill a need, in this case finding cheaper gas. Thus, it should talk to other autonomous agents, such as:

  • calendar
  • law enforcement to grade destination
  • vehicle network for fuel requirements
  • traffic
  • mapper
  • GPS
  • weather
  • retail for
    • quality
    • complaints
    • hours of operation
    • costs
  • Social Net
  • Politics
  • Financial
  • map routing, and so forth.

It should also be informed by human agents in a trusted relationship with the user. What we then have is An Ad Hoc Dynamic Network of Social Agent Recommenders (AhDyNoSAR).

The Mind Map Diagram below gives a contextual view of this idea.

SynergisticMobileAgentSocialNet Mind Map

SynergisticMobileAgentSocialCloud Mind Map (click for larger view)

Let’s look at another example. Someone is walking in neighborhood that has a few restaurants. The embedded Agent notes that the last time the person ate was a few hours ago (based on shopping venue, Calendar, etc.). The shop’s agents are contacted and a decision processing workspace is created. Is the person currently viable, do they have cash or credit available? Each store will check inventory and accounting ratios, does it need to offer a discount or promotion to this person? More agents mobilize to assert their criteria. What are the person’s tastes, dietary restrictions and allergies, past intake (who wants pizza twice in one day?), and other multidimensional agents in a problem space hierarchy are evoked.

After all agents complete their reckonings and the spontaneous net reaches a stable resonance, the person’s intimate personal soft computing agents make a decision. It turns out that the person is currently following their spiritual observance and is fasting today. This result is sent into the local agent milieu and starts a new search for resonance, so no food, how about some clothing or reading material? Again a new recommendation graph is created, religious and political leanings are queried, clothing and accessory rules are fired, ah, that is a very old turban, here are some suggestions.

Unfortunately, the person has now walked into a new map space, a neighborhood park. Now new agents awake: social engagement, entertainment, sexual, defensive.

Interface

It would be so gross if the information that this new cloud offers is shown as ads. A better approach is that this information space is entered as a virtual world, using technologies like that of Massively multiplayer online role-playing game (MMORPG). The consumer becomes an Avatar moving through Recommendation Space, a superimposed view on current locality based environments. Instead of or in addition to other consumers, the other characters are the various agents most visible recommendation goal.

Antagonistic

Unlike Apps an Agent should always be considered adversarial. That is, even when an agent provides a benefit, it also can allow intentionally or via weaknesses a loss of security and privacy since it must negotiate information with other agents. Thus, though current or future standards may be used, they must be in virtual application spaces that use encrypted anonymous data. This will be just as virus and other malware, an ongoing battle.

Collaboration

It would not be optimal to require a download of an agent to each user’s location or device. Instead, agents will exist in the cloud as a multi-agent system. A user will have a private cloud virtual machine and address space for agent storage and recommendation space. To handle disconnected use, an agent will have a mobile agent shadow. It will provide simple assistance and will punt decisions and actions it cannot handle until connection to the cloud is established.

Monetization

With Apps, the app provider may require purchase or try to enforce lock-in or an advertising monopoly. This can also be accomplished by centralizing the App marketplace. This may not work directly with Agents. Agents may not even provide an obvious visible function. For example, an agent may just contribute parking meter locations and status to other agents that use a map agent.

In the real world eventually someone has to pay the piper. So too will the development and use of agents must be rewarded. Some options are:

  • Advertising:
    An agent can contribute to an advertising stream that ultimately reaches the consumer facing user interface device.
  • Agent micropayments:
    Agents will negotiate among their collaborators to maintain a balance of payments, an agent of agents, and this payment is satisfied by the user or the user’s fee structure that the network provider maintains.
  • Purchase:
    The consumer will purchase agents. If the fidelity and number of agents is adequate the quality of service is greater.
  • Other.

Dangers

Security

Of course, the internet is currently wide open and thus this opens up predation to another level if Agent “sandboxes” are porous, if personal data is not secure.

Privacy

The present cavalier attitudes regarding personal privacy exhibited by the large Internet service providers is a big warning sign that giving agents access to even more information would be just another data mining delicacy ripe for exploitation.

Future

And now for an even more far out scenario. In a classic Science Fiction novel, before a character dies, a copy of their knowledge is captured. This intelligence is then available for implantation into someone as an “Aspect”, an agent that can add its unique expertise and judgment to the human host. That is a more radical direct means for accomplishing something that the social networking may evolve into, a means to collect knowledge and translate that into a ubiquitous intelligence.

Conclusion

Presented was a critique of conventional app centric mobile computing and a suggestion that Agent technology can provide a more realistic computing environment. The term Agent was not defined here. Perhaps the difference with an App is just intent or where the output is ingested. The experts are still debating Agent technology and its applications.

Updates

Acknowledgments

Further Reading

All rights reserved. No part of this document may be reproduced or transmitted in any form by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from Josef Betancourt.

This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License, See: http://creativecommons.org/licenses/by-nc-nd/3.0/

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