Automated Negotiation on Internet Agent-Based Markets: Discussion

Transcription

1 Automated Negotiation on Internet Agent-Based Markets: Discussion Benoît LELOUP École Nationale Supérieure des Télécommunications de Bretagne & Institut des Applications Avancées de l Internet (IAAI - Marseille)

2 Intelligent Agents Intelligent agents are software abstractions that are able to communicate and make decisions in an (semi) autonomous way. Such software are also called autonomous agents (AI)

3 Agent Technology for E-commerce transactions Shopbots (Shopping Agents) Pricebots (Selling Agents) Auctionbots (Auctions) Negotiating Agents: bargaining, negotiation

4 Agents software Selling Shopping Search for products Comparisons of prices Processing product information Contract and buy on the behalf of their owner (Buying Agents) Improve the productivity of online business interactions Allow a seller to monitor & process several parallel commercial transactions (negotiations) thus increasing his profits MFI 03

5 Transactions mechanisms Transactions mechanisms? Auctions Dynamic posted prices Bargaining Currently, the Internet mainly involves simple negotiation protocols as posted prices or auctions

6 Heuristics vs. DP?? MFI 03

7 Heuristics (procedural knowledge) ( ) criteria, methods, or principles for deciding which among several alternative courses of action promises to be the most effective in order to achieve some goal Pearl (1984) Modern meaning: procedure that may give an optimal (best) solution to a problem but offers no guarantee of doing so. If it can be proven that an exact solution exists, then this becomes an algorithm

8 Heuristics-based decision making People base their negotiation policies on simple heuristics Empirical evidences in Psychology (Pruitt 1981) Empirical evidences in Economics (Simon 1992) Evolutionary Game Theory (Axelrod 1984) Most of the literature on E-commerce multi-agent systems (Chavez and Maes 1996)

9 Negotiation Heuristics A negotiation heuristic h i may be defined as, A starting offer: the largest offer a seller can make when he follows heuristic h i A predetermined rule of thumb (e.g., mimetic behavior)

10 Heuristics and DP A rule of thumb is a mapping from a subset of the states of the negotiation process into the selling agent s action space (e.g., concede, do not concede, agree, do not agree) If we impose no further restrictions, like limited complexity or bounded rationality (Simon [1992]), then implementing the DP solution may be viewed as a particular rule of thumb

11 Timing of the decision process Sequence of negotiations t 1 t t + 1 Customers arrivals dates Maximal duration of a negotiation - Parallel inter-temporal decision problems are allowed - Do not allow a seller to engage parallel negotiation processes with a same customer

12 Important features for agent-based electronic marketplace models Heuristics and dynamic programming approaches together (must be viewed as complementary approaches) Polymorph population due to at least two factors: artificial agents (with various technologies) and human agents (exhibiting various behaviors) The horizon of time should be stochastic rather than finite (to avoid ending effects and rationality paradox) Better to follow an interactive decision theory approach (less exigent about the information the agents have about each other) rather than a game theory one: CK is too strong in a polymorph population composed by human and artificial agents

13 Important features for agent-based electronic marketplaces models Both allow for on line learning and for off line learning Elicit probabilities of agreement (negotiation) when off line learning is possible via laboratory experiments and/or multi-agent systems simulations Sophisticated customers behaviors must be taken into account: probabilities of agreement that are associated to each heuristic are not necessarily constant with the number of times a seller used them (e.g., situations in which customers have learning and/or punishment and/or communication skills) MFI 03

14 Important features for agent-based electronic marketplaces models For operational purposes, policies that are based on Bellman dynamic programming equations are usually not appropriate (intractability of the optimal solution) => focus on monitoring policies that are both tractable and computable in reasonable time Define the topology of the network interactions between customers (i.e., take into account social interactions) A concept of World neighborhood may be pertinent but one must be cautious: the diffusion rate of the information within a population of customers depends on their connectivity and on the size of the population MFI 03

15 Interaction structures (two dimensional lattices) von Neumann Moore Extended Moore / von Neumann World Extended Moore

16 Artificial vs. Artificial Agents are endowed with some predetermined negotiation heuristics Arrivals BA 1 Neighbors of BA i BA 2 BA k BA n SA Negotiation process BA i Agent i communicates its information to its neighborhood SA: Selling agent BA i : Buying agent i

17 Restructuring a negotiation process: human and artificial Agents are endowed with some predetermined negotiation heuristics Arrivals BA 1 Neighbors of BA i BA 2 BA k Ba n uman / Expert? SA Negotiation process BA i CB Multi-negotiation objects (Fixed sized) CB Pool of information for SA (knowledge database) Similar markets Agent i communicates its information to its neighborhood? Pool of information for BA (knowledge database) Human / Expert SA: Selling agent BA i : Buying agent i CB: Case-based decision makin

18 Questions to be addressed The negotiation object is a single issue (the price) while it may cover hundreds of issues [related to price, quality, timings, penalties, ( ), etc.]. Jennings et al. (2001) Need to extend models to multi-negotiation objects Asking why an agent is endowed with some particular rules of thumb or whether how and why they might be changed in the course of time Economic efficiency of the aggregate (agent-based) market Dynamics of the prices one can observe with specific transactions protocols, negotiation heuristics, information patterns, etc. Effect of competition among sellers on their monitoring algorithm