The Royal Academy of Engineering has released a report on the social, legal and ethical issues involving autonomous systems — systems that are adaptive, learn and can make decisions without the intervention or supervision of a human.

The report, Autonomous Systems: Social, Legal and Ethical Issues (pdf), was based on a roundtable discussion “from a wide range of experts, looking at the areas where autonomous systems are most likely to emerge first, and discussing the broad ethical issues surrounding their uptake.”

While autonomous systems have broad applicability, the report focuses on two areas: transportation (e.g. autonomous road vehicles) and personal care (e.g., smart homes).

“Autonomous systems, such as fully robotic vehicles that are “driverless” or artificial companions that can provide practical and emotional support to isolated people, have a level of self-determination and decision making ability with the capacity to learn from past performance. Autonomous systems do not experience emotional reactions and can therefore perform better than humans in tasks that are dull, risky or stressful. However they bring with them a new set of ethical problems. What if unpredicted behaviour causes harm? If an unmanned vehicle is involved in an accident, who is responsible – the driver or the systems engineer? Autonomous vehicles could provide benefits for road transport with reduced congestion and safety improvements but there is a lack of a suitable legal framework to address issues such as insurance and driver responsibility.
…
The technologies for smart homes and patient monitoring are already in existence and provide many benefits to older people, such as allowing them to remain in their own home when recovering from an illness, but they could also lead to isolation from family and friends. Some users may be unfamiliar with the technologies and be unable to give consent to their use.”

The RAEng report recommends “engaging early in public consultation” and working to establish “appropriate regulation and governance so that controls are put in place to guide the development of these systems”.

rdf:SeeAlso Autonomous tech ‘requires debate’; Scientists ponder rules and ethics of robo helpers; Robot cats could care for older Britons.

(via Mike Wooldridge)

Moore argues that in competitive situations, this can drive those offering advice to increasingly exaggerate how sure they are. And it spells bad news for scientists who try to be honest about gaps in their knowledge.
…
In Moore’s experiment, volunteers were given cash for correctly guessing the weight of people from their photographs. In each of the eight rounds of the study, the guessers bought advice from one of four other volunteers. The guessers could see in advance how confident each of these advisers was (see table), but not which weights they had opted for.
…
Describing his work at an Association for Psychological Science meeting in San Francisco last month, Moore said that following the advice of the most confident person often makes sense, as there is evidence that precision and expertise do tend to go hand in hand. For example, people give a narrower range of answers when asked about subjects with which they are more familiar”

Why aren’t we better at recognizing cover-confidence? There must be some evolutionary fitness in this, at least for humans. There can be a big penalty in indecision or vacillation. I wonder if we will see the same phenomenon in systems of cooperating autonomous agents?

Here’s the paper:

Joseph R. Radzevick and Don A. Moore, Competing To Be Certain (But Wrong): Social Pressure and Overprecision in Judgment, 21st Annual Convention of the Association for Psychological Science, May 2009.

Overprecision in judgment is both the most robust and the least understood form of overconfidence. Overly precise judgments claim more certainty than is objectively warranted. In this paper, we investigate whether the competitive social pressure of a market contributes to overprecision among those competing for influence. We find evidence that markets do indeed exacerbate overprecision. This evidence comes from two experiments in which advisors attempt to sell their advice. In the first experiment, advisors must compete with other advice sellers. In the second, advisors and decision makers are paired. Overprecision exists in both studies, and it helps advisors’ sell their advice. However, the market also exacerbates overprecision. We discuss the strategic implications of these results.

“A “wave” is equal parts conversation and document, where people can communicate and work together with richly formatted text, photos, videos, maps, and more. Here’s how it works: In Google Wave you create a wave and add people to it. Everyone on your wave can use richly formatted text, photos, gadgets, and even feeds from other sources on the web. They can insert a reply or edit the wave directly. It’s concurrent rich-text editing, where you see on your screen nearly instantly what your fellow collaborators are typing in your wave. That means Google Wave is just as well suited for quick messages as for persistent content — it allows for both collaboration and communication. You can also use “playback” to rewind the wave and see how it evolved.”

Google Wave is not available yet, but you can sign up to be notified when it’s launched.

Here’s a random thought. Our models for communication in multiagent systems (e.g., KQML and FIPA) were informed by if not based on email and, to a lesser degree, IM. If Wave is a useful new communication model for humans, does it have a counterpart for software agents? If so, I suspect that ideas from the Semantic Web will be useful to provide a “rich content” for agents.

For more views, see posts by o’reilly, techcrunch, BusinessWeek and Gabor Cselle.

Olga Vladi Ratsimor, Opportunistic Bartering of Digital Goods and Services in Pervasive Environments, Ph.D. Dissertation in Computer Science, University of Maryland, Baltimore County, August 2007.

The vision of mobile personal devices querying peers in their environment for information such as local restaurant recommendations or directions to the closest gas station, or traffic and weather updates has long been a goal of the pervasive research community. However, considering the diversity and the personal nature of devices participating in pervasive environments it is not feasible to assume that these interactions and collaborations will take place with out economically-driven motivating incentives.

This dissertation presents a novel bartering communication model that provides an underlying framework for incentives for collaborations in mobile pervasive environments by supporting opportunistic serendipitous peer-to-peer bartering for digital goods such as ring tones, MP3’s and podcasts.

To demonstrate viability and advantages of this innovative bartering approach, we compare and contrast the performances of two conventional, frequently employed, peer-to-peer interaction approaches namely Altruists and FreeRiders against two collaborative strategies that employ the Double Coincidence of Wants paradigm from the domain of barter exchanges. In particular, we present our communication framework that represents these collaborative strategies through a set of interaction policies that reflect these strategies. Furthermore, we present a set of results from our in-depth simulation studies that compare these strategies.

We examine the operation of the nodes employing our framework and executing these four distinct strategies and specifically, we compare the performances of the nodes executing these strategies in homogeneous and heterogeneous networks of mobile devices. We also examine the effects of adding InfoStations to these networks. For each of the strategies, we observe levels of gains and losses that nodes experience as result of collaborative digital good exchanges. We also evaluate communication overhead that nodes incur while looking for possible collaborative exchange. Furthermore, this dissertation offers an in-depth study of the swarm-like inter-strategy dynamics in heterogeneous networks populated with diverse nodes displaying varying levels of collaborative interaction attitudes. Further, the bartering framework is extended by incorporating value-sensitive bartering models that incorporate digital goods and content valuations into the bartering exchange process. In addition, the bartering model is extended by integration of socially influenced collaborative interaction that exploit role based social relationships between mobile peers that populate dynamic mobile environments.

Taken as a whole, the novel research work presented in this dissertation offers the first comprehensive effort that employs and models opportunistic bartering-based collaborative methodology in the context of serendipitous encounters in dynamic mobile peer-to-peer pervasive environments where mobile entities negotiate and exchange digital goods and content.

Reporter Will Pavia of The Times was one of the judges a the 2008 Loebner Prize competition last week. In a story in The Times yesterday, Machine takes on man at mass Turing Test, he revealed his question that gave away one of the cold, lifeless, mechanical bots.

“The other correspondent was undoubtedly a robot. I asked it for its opinion on Sarah Palin, and it replied: ‘Sorry, don’t know her.’ No sentient being could possibly answer in this way.”

Of course, this could have been an ironic response from a clever person who was mocking VP candidate Palin’s stock question of “Who is Barack Obama?”.

(spotted on Languae Log)

“The Loebner Prize is an annual competition that awards prizes to the Chatterbot considered by the judges to be the most humanlike of those entered. The format of the competition is that of a standard Turing test. In the Loebner Prize, as in a Turing test, a human judge is faced with two computer screens. One is under the control of a computer, the other is under the control of a human. The judge poses questions to the two screens and receives answers. Based upon the answers, the judge must decide which screen is controlled by the human and which is controlled by the computer program.”

This year, the competition is taking place ar Reading under the direction of Professor Kevin Warwick. The thirteen initial entries which have been reduced to six finalists.

The competition was started in 1990 by Hugh Loebner, who put up a set of cash prizes, including one worth $100,000 for the “first chatterbot that judges cannot distinguish from a real human in a Turing test that includes deciphering and understanding text, visual, and auditory input.” A fact of local interest is that Hugh Loebner worked at UMBC as the assistant director of computing in the 1980s. He left UMBC to run his family’s business, which at the time was doing well manufacturing roll-up disco dance floors for parties.

Over the years the Loebner prize competitions has come under considerable criticism from the AI research community. A common option among AI researchers is that the competition is more about publicity than science and encourages people to try to do well by exploiting tricks and competition-specific strategies rather than work on the fundamental problems underlying the development of intelligent machines. This article in Salon, Artificial stupidity, summarizes the positions.

Here are some stories on the 2008 Loebner Prize competition in the press: ‘Intelligent’ computers put to the test’, Invasion of ‘human’ robots and Artificial Conversational Entities: Can A Machine Act Human and Be Given ‘Rights’?.

“FIPA is currently working with the OMG on agent standardization, including an SOA standard that includes agents (SOA-Pro) and an Agent Metamodel and Profile (AMP). The Agent Metamodel and Profile RFP has many companies that are participating, including (but not limited to): HP, Unisys, CSC, Deere & Co, Thales, Metropolitan Life, SINTEF, and DFKI. If you are interested in participating, please let me know.

Any comments on the Agent Metamodel and Profile (AMP) RFP are welcomed. (The above companies and RMIT have already submitted their suggestions. The current release can be downloaded from: http://www.omg.org/cgi-bin/doc?ad/2008-06-02”

The OMG Agent Platform Special Interest Group page maintains links to documents about these emerging agent standards.

Paul Ormerod and Greg Wiltshire have a preprint of a paper ‘Binge’ drinking in the UK: a social network phenomenon (pdf) that reports on a study that the binge drinking phenomenon seems to spread through “small world” social networks rather than by imitating influentials in a “scale free” network

“We analyse the recent rapid growth of ‘binge’ drinking in the UK. This means the consumption of large amounts of alcohol, especially by young people, leading to serious anti-social and criminal behaviour in urban centres. We show how a simple agent-based model, based on binary choice with externalities, combined with a small amount of survey data can explain the phenomenon. We show that the increase in binge drinking is a fashion-related phenomenon, with imitative behaviour spreading across social networks. The results show that a small world network, rather than a random or scale free, offers the best description of the key aspects of the data.”

It’s fascinating that with the right data, simulation models can help to answer such questions.

“Edd Hifeng barely merits a second glance in “Second Life.” A steel-gray robot with lanky limbs and linebacker shoulders, he looks like a typical avatar in the popular virtual world. But Edd is different.

His actions are animated not by a person at a keyboard but by a computer. Edd is a creation of artificial intelligence, or AI, by researchers at Rensselaer Polytechnic Institute, who endowed him with a limited ability to converse and reason. It turns out “Second Life” is more than a place where pixelated avatars chat, interact and fly about. It’s also a frontier in AI research because it’s a controllable environment where testing intelligent creations is easier.

“It’s a very inexpensive way to test out our technologies right now,” said Selmer Bringsjord, director of the Rensselaer Artificial Intelligence and Reasoning Laboratory.”

There’s more information in an article on Virtual World News. Apparently the goal is not to build interesting Second Life Bots using a variety of hacks, but to demonstrate human-like behaviour using more principled techniques.

“RPI is looking, initially, at a “theory of mind” for children, specifically with a false-belief test. In the real world, a child (age 4) would be shown a person placing a teddy bear in a cabinet. When the first person leaves, a second person would move the bear to another spot, like a refrigerator. When asked where the first person will look for the bear, they usually answer with the refrigerator due to a lack of understanding of other people. In Second Life, an automated theorem prover and procedures for converting conversational English into formal logic make up the brain of “Eddie,” the four-year-old avatar. When posed the above problem, Eddie responded as the human child would.”

Here’s a video of Eddie in action.